7.1.7 Feature and Object Detection Systems

Tzafestas, C.S.[Costas S.], Maragos, P.[Petros],
Shape Connectivity: Multiscale Analysis and Application to Generalized Granulometries,
JMIV(17), No. 2, September 2002, pp. 109-129.
DOI Link 0211
BibRef

Dougherty, E.R.[Edward R.],
Granulometric Size Density for Segmented Random-Disk Models,
JMIV(17), No. 3, November 2002, pp. 271-281.
DOI Link 0211
BibRef

Caselles, V.[Vicent], Monasse, P.[Pascal],
Grain Filters,
JMIV(17), No. 3, November 2002, pp. 249-270.
DOI Link 0211
BibRef

Pang, G.K.H., Liu, H.H.S.,
LED location beacon system based on processing of digital images,
ITS(2), No. 3, September 2001, pp. 135-150.
IEEE Abstract. 0402
BibRef

Sinzinger, E.D.[Eric D.],
Radial segmentation,
PRL(25), No. 12, September 2004, pp. 1337-1350.
Elsevier DOI 0409
To partition circular regions.
See also model-based approach to junction detection using radial energy, A. BibRef

Xiao, Z.T.[Zhi-Tao], Hou, Z.X.[Zheng-Xin],
Phase based feature detector consistent with human visual system characteristics,
PRL(25), No. 10, 16 July 2004, pp. 1115-1121.
Elsevier DOI 0407
BibRef

Jiang, J.M.[Jian-Min], Weng, Y.[Ying], Li, P.J.[Peng-Jie],
Dominant colour extraction in DCT domain,
IVC(24), No. 12, 1 December 2006, pp. 1269-1277.
Elsevier DOI 0610
Dominant colour features; MPEG-7; Feature extraction in compressed domain Without decompressing. BibRef

Marks, R.L.[Richard L.],
Method for color transition detection,
US_Patent7,113,193, Sep 26, 2006
WWW Link. Detect object via color BibRef 0609

Matei, B., Meignen, S.,
Nonlinear and Nonseparable Bidimensional Multiscale Representation Based on Cell-Average Representation,
IP(24), No. 11, November 2015, pp. 4570-4580.
IEEE DOI 1509
Approximation methods BibRef

Urbach, E.R., Roerdink, J.B.T.M.[Jos B.T.M.], Wilkinson, M.H.F.[Michael H.F.],
Connected Shape-Size Pattern Spectra for Rotation and Scale-Invariant Classification of Gray-Scale Images,
PAMI(29), No. 2, February 2007, pp. 272-285.
IEEE DOI 0701
BibRef
Earlier:
Connected rotation-invariant size-shape granulometries,
ICPR04(I: 688-691).
IEEE DOI 0409
BibRef

Urbach, E.R.[Erik R.],
Intelligent Object Detection Using Trees,
ISMM15(289-300).
Springer DOI 1506
BibRef

Land, S.[Sander], Wilkinson, M.H.F.[Michael H.F.],
A Comparison of Spatial Pattern Spectra,
ISMM09(92-103).
Springer DOI 0908
BibRef

Wilkinson, M.H.F.,
Generalized pattern spectra sensitive to spatial information,
ICPR02(I: 21-24).
IEEE DOI 0211
BibRef

Broadwater, J.[Joshua], Chellappa, R.[Rama],
Hybrid Detectors for Subpixel Targets,
PAMI(29), No. 11, November 2007, pp. 1891-1903.
IEEE DOI 0711
In hyperspectral imagery analysis. Model background using physics and statistics. Compare to AMSD and ACE. BibRef

Zhang, M.J.[Meng-Jie], Bhowan, U.[Urvesh], Ny, B.[Bunna],
Genetic Programming for Object Detection: A Two-Phase Approach with an Improved Fitness Function,
ELCVIA(6), No. 1, 2007, pp. 27-43.
DOI Link 0709
Genetic programming to generate code applied in windows across the image to extract objects. BibRef

Clarke, T.A.[Timothy Alan], Wang, X.C.[Xin-Chi],
Method for identifying measuring points in an optical measuring system,
US_Patent7,184,151, Feb 27, 2007
WWW Link. BibRef 0702

Gutierrez, J.A.[José A.], Armstrong, B.S.R.[Brian S.R.],
Precision Landmark Location for Machine Vision and Photogrammetry: Finding and Achieving the Maximum Possible Accuracy,
Springer2008, ISBN: 978-1-84628-912-5.
WWW Link. Code, Landmarks. Techniques to achieve optimal results. BibRef 0800

Rosin, P.L.[Paul L.],
A simple method for detecting salient regions,
PR(42), No. 11, November 2009, pp. 2363-2371.
Elsevier DOI 0907
Salience map; Importance map; Focus of attention; Distance transform BibRef

Gopalakrishnan, V.[Viswanath], Hu, Y.Q.[Yi-Qun], Rajan, D.[Deepu],
Salient Region Detection by Modeling Distributions of Color and Orientation,
MultMed(11), No. 5, 2009, pp. 892-905.
IEEE DOI 0907
BibRef

Gopalakrishnan, V.[Viswanath], Hu, Y.Q.[Yi-Qun], Rajan, D.[Deepu],
Random Walks on Graphs for Salient Object Detection in Images,
IP(19), No. 12, December 2010, pp. 3232-3242.
IEEE DOI 1011
BibRef
Earlier:
Random walks on graphs to model saliency in images,
CVPR09(1698-1705).
IEEE DOI 0906
BibRef

Gopalakrishnan, V.[Viswanath], Rajan, D.[Deepu], Hu, Y.Q.[Yi-Qun],
A Linear Dynamical System Framework for Salient Motion Detection,
CirSysVideo(22), No. 5, May 2012, pp. 683-692.
IEEE DOI 1202
BibRef
Earlier: A1, A3, A2:
Sustained Observability for Salient Motion Detection,
ACCV10(III: 732-743).
Springer DOI 1011
BibRef
And: A1, A3, A2:
Unsupervised Feature Selection for Salient Object Detection,
ACCV10(II: 15-26).
Springer DOI 1011
BibRef

Tuytelaars, T.[Tinne], Lampert, C.H.[Christoph H.], Blaschko, M.B.[Matthew B.], Buntine, W.[Wray],
Unsupervised Object Discovery: A Comparison,
IJCV(88), No. 2, June 2010, pp. xx-yy.
Springer DOI 1003
BibRef

Sharmanska, V.[Viktoriia], Quadrianto, N.[Novi], Lampert, C.H.[Christoph H.],
Augmented Attribute Representations,
ECCV12(V: 242-255).
Springer DOI 1210
BibRef

Ozdemir, B.[Bahadir], Aksoy, S.[Selim], Eckert, S.[Sandra], Pesaresi, M.[Martino], Ehrlich, D.[Daniele],
Performance measures for object detection evaluation,
PRL(31), No. 10, 15 July 2010, pp. 1128-1137.
Elsevier DOI 1008
Performance evaluation; Object detection; Object matching; Shape modeling; Multi-criteria ranking BibRef

Chen, J.[Jie], Shan, S.G.[Shi-Guang], He, C.[Chu], Zhao, G.Y.[Guo-Ying], Pietikainen, M., Chen, X.L.[Xi-Lin], Gao, W.[Wen],
WLD: A Robust Local Image Descriptor,
PAMI(32), No. 9, September 2010, pp. 1705-1720.
IEEE DOI 1008
Weber Local Descriptor (human perception depends not only on the change, but the initial level). WLD: differential excitation and orientation. Apply to variety of feature detections. BibRef

Matsumoto, M.[Mitsuharu],
Self-Quotient epsilon-Filter for Feature Extraction from Noise Corrupted Image,
IEICE(E93-D), No. 11, November 2010, pp. 3066-3075.
WWW Link. 1011
BibRef

Gu, Y.F.[Yan-Feng], Wang, C.[Chen], Wang, S.Z.[Shi-Zhe], Zhang, Y.[Ye],
Kernel-based regularized-angle spectral matching for target detection in hyperspectral imagery,
PRL(32), No. 2, 15 January 2011, pp. 114-119.
Elsevier DOI 1101
Hyperspectral imagery; Target detection; Spectral matched filter; Spectral angle mapper; Kernel methods BibRef

Khachaturov, G.[Georgii],
A scalable, high-precision, and low-noise detector of shift-invariant image locations,
PRL(32), No. 2, 15 January 2011, pp. 145-152.
Elsevier DOI 1101
Feature detection; Shift invariance; Multi-scale processing; Image-to-data structures processing BibRef

Lemaitre, C., Perdoch, M., Rahmoune, A., Matas, J.G., Miteran, J.,
Detection and matching of curvilinear structures,
PR(44), No. 7, July 2011, pp. 1514-1527.
Elsevier DOI 1103
Curvilinear structures; Wiry objects; Descriptor; Detector; Segmentation; Matching BibRef

Lemaitre, C.[Cédric], Miteran, J.[Johel], Matas, J.G.[Jiri G.],
Definition of a Model-Based Detector of Curvilinear Regions,
CAIP07(686-693).
Springer DOI 0708
BibRef

Murray, P., Marshall, S.,
A New Design Tool for Feature Extraction in Noisy Images Based on Grayscale Hit-or-Miss Transforms,
IP(20), No. 7, July 2011, pp. 1938-1948.
IEEE DOI 1107
BibRef

Vedaldi, A.[Andrea], Zisserman, A.[Andrew],
Efficient Additive Kernels via Explicit Feature Maps,
PAMI(34), No. 3, March 2012, pp. 480-492.
IEEE DOI 1201
BibRef
Earlier: CVPR10(3539-3546).
IEEE DOI 1006
BibRef

Vempati, S.[Sreekanth], Vedaldi, A.[Andrea], Zisserman, A.[Andrew], Jawahar, C.V.,
Generalized Rbf feature maps for Efficient Detection,
BMVC10(xx-yy).
HTML Version. 1009
BibRef

Vedaldi, A.[Andrea], Zisserman, A.[Andrew],
Sparse kernel approximations for efficient classification and detection,
CVPR12(2320-2327).
IEEE DOI 1208
BibRef

Vedaldi, A.[Andrea], Gulshan, V.[Varun], Varma, M.[Manik], Zisserman, A.[Andrew],
Multiple kernels for object detection,
ICCV09(606-613).
IEEE DOI 0909

See also Learning The Discriminative Power-Invariance Trade-Off. BibRef

Chatfield, K.[Ken], Simonyan, K.[Karen], Vedaldi, A.[Andrea], Zisserman, A.[Andrew],
Return of the Devil in the Details: Delving Deep into Convolutional Nets,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Chatfield, K.[Ken], Lempitsky, V.[Victor], Vedaldi, A.[Andrea], Zisserman, A.[Andrew],
The devil is in the details: An evaluation of recent feature encoding methods,
BMVC11(xx-yy).
HTML Version. 1110
Award, BMVC, HM Poster. BibRef

Kompella, V.R.[Varun Raj], Sturm, P.F.[Peter F.],
Collective-reward based approach for detection of semi-transparent objects in single images,
CVIU(116), No. 4, April 2012, pp. 484-499.
Elsevier DOI 1202
Collective-reward; Object detection; Semi-transparency; Transparency; Glass. Both transmission and reflection. BibRef

Liu, S.W.[Shang-Wang], He, D.J.[Dong-Jian], Liang, X.H.[Xin-Hong],
An Improved Hybrid Model for Automatic Salient Region Detection,
SPLetters(19), No. 4, April 2012, pp. 207-210.
IEEE DOI 1203
BibRef

Shi, R., Liu, Z., Du, H., Zhang, X., Shen, L.,
Region Diversity Maximization for Salient Object Detection,
SPLetters(19), No. 4, April 2012, pp. 215-218.
IEEE DOI 1203
BibRef

Kobayashi, T.[Takumi], Otsu, N.[Nobuyuki],
Motion Recognition Using Local Auto-Correlation of Space-Time Gradients,
PRL(33), No. 9, 1 July 2012, pp. 1188-1195.
Elsevier DOI 1202
BibRef
Earlier:
Image Feature Extraction Using Gradient Local Auto-Correlations,
ECCV08(I: 346-358).
Springer DOI 0810
Motion recognition; Motion feature extraction; Space-time gradient; Auto-correlation; Bag-of-features
See also Face Recognition System Using Local Autocorrelations and Multiscale Integration.
See also Gesture Recognition Using Auto-Regressive Coefficients of Higher-Order Local Auto-Correlation Features. BibRef

Yoo, J.C., Ahn, C.W.,
Image matching using peak signal-to-noise ratio-based occlusion detection,
IET-IPR(6), No. 5, 2012, pp. 483-495.
DOI Link 1210
locate objects with partial occlusions. Compare to correlation based methods. BibRef

Zheng, Z.[Zhong], Wei, L.[Lu], Hamalainen, J., Tirkkonen, O.,
A Blind Time-Reversal Detector in the Presence of Channel Correlation,
SPLetters(20), No. 5, May 2013, pp. 459-462.
IEEE DOI 1304
BibRef

Kong, Y.[Yan], Dong, W.M.[Wei-Ming], Mei, X.[Xing], Zhang, X.P.[Xiao-Peng], Paul, J.C.[Jean-Claude],
SimLocator: robust locator of similar objects in images,
VC(29), No. 9, September 2013, pp. 861-870.
WWW Link. 1307
BibRef

Zhang, X.[Xin], Yang, Y.H.[Yee-Hong], Han, Z.G.[Zhi-Guang], Wang, H.[Hui], Gao, C.[Chao],
Object class detection: A survey,
Surveys(46), No. 1, October 2013, pp. Article No 10.
DOI Link 1311
Survey, Object Class. Object class detection, also known as category-level object detection, has become one of the most focused areas in computer vision in the new century. This article attempts to provide a comprehensive survey of the recent technical achievements. BibRef

Verdié, Y.[Yannick], Lafarge, F.[Florent],
Detecting parametric objects in large scenes by Monte Carlo sampling,
IJCV(106), No. 1, January 2014, pp. 57-75.
WWW Link. 1402
BibRef
Earlier:
Efficient Monte Carlo Sampler for Detecting Parametric Objects in Large Scenes,
ECCV12(III: 539-552).
Springer DOI 1210
Sampling rather than all points. BibRef

Niitsu, Y.S.[Yasu-Shi], Iizuka, T.[Takaaki],
Improving light marker accuracy on camera images,
SPIE(Newsroom), February 18, 2014
DOI Link 1402
A novel method determines precise boundaries of the light markers used to find the center of a target in image processing applications. BibRef

Yang, H.G.[Hui-Guang], Ahuja, N.[Narendra],
Automatic segmentation of granular objects in images: Combining local density clustering and gradient-barrier watershed,
PR(47), No. 6, 2014, pp. 2266-2279.
Elsevier DOI 1403
Image segmentation BibRef

Zimmermann, K.[Karel], Hurych, D.[David], Svoboda, T.[Tomáš],
Non-Rigid Object Detection with Local Interleaved Sequential Alignment (LISA),
PAMI(36), No. 4, April 2014, pp. 731-743.
IEEE DOI 1404
BibRef
Earlier:
Exploiting Features: Locally Interleaved Sequential Alignment for Object Detection,
ACCV12(I:446-459).
Springer DOI 1304
Computational modeling BibRef

Peng, X.M.[Xiao-Ming],
Combine color and shape in real-time detection of texture-less objects,
CVIU(135), No. 1, 2015, pp. 31-48.
Elsevier DOI 1504
Real-time texture-less object detection BibRef

Li, C.[Chao], Peng, X.M.[Xiao-Ming],
Joint Transformer and Mamba fusion for multispectral object detection,
IVC(156), 2025, pp. 105468.
Elsevier DOI Code:
WWW Link. 2503
Multispectral object detection, Transformer, Mamba, Feature fusion, Visible-infrared images BibRef

Chong, N.S.[Nguan Soon], Kho, Y.H.[Yau Hee], Wong, M.L.D.[Mou Ling Dennis],
Visual detection in omnidirectional view sensors,
SIViP(9), No. 4, May 2015, pp. 923-940.
Springer DOI 1504
BibRef

Diebold, J.[Julia], Tari, S.[Sibel], Cremers, D.[Daniel],
The Role of Diffusion in Figure Hunt Games,
JMIV(52), No. 1, May 2015, pp. 108-123.
Springer DOI 1505
Finding waldo. BibRef

Han, X.H.[Xian-Hua], Chen, Y.W.[Yen-Wei], Xu, G.[Gang],
High-Order Statistics of Weber Local Descriptors for Image Representation,
Cyber(45), No. 6, June 2015, pp. 1180-1193.
IEEE DOI 1506
Adaptation models BibRef

Han, X.H.[Xian-Hua], Chen, Y.W.[Yen-Wei],
HEp-2 Staining Pattern Recognition Using Stacked Fisher Network for Encoding Weber Local Descriptor,
PR(63), No. 1, 2017, pp. 542-550.
Elsevier DOI 1612
HEp-2 image representation BibRef
Earlier: Add A3: Xu, G.[Gang], MLMI15(85-93).
Springer DOI 1511
BibRef

Gao, L.[Lianru], Yang, B.[Bin], Du, Q.[Qian], Zhang, B.[Bing],
Adjusted Spectral Matched Filter for Target Detection in Hyperspectral Imagery,
RS(7), No. 6, 2015, pp. 6611.
DOI Link 1507
BibRef

Santosh, K.C., Wendling, L.[Laurent], Antani, S.K.[Sameer K.], Thoma, G.R.[George R.],
Overlaid Arrow Detection for Labeling Regions of Interest in Biomedical Images,
IEEE_Int_Sys(31), No. 3, May 2016, pp. 66-75.
IEEE DOI 1606
BibRef
Earlier:
Scalable Arrow Detection in Biomedical Images,
ICPR14(3257-3262)
IEEE DOI 1412
Biomedical imaging BibRef

Hong, J.K.[Jong-Kwang], Hong, Y.W.[Yong-Won], Uh, Y.J.[Young-Jung], Byun, H.R.[Hye-Ran],
Discovering overlooked objects: Context-based boosting of object detection in indoor scenes,
PRL(86), No. 1, 2017, pp. 56-61.
Elsevier DOI 1702
Object detection BibRef

Wang, S.P.[Shi-Ping], Huang, A.P.[Ai-Ping],
Salient object detection with low-rank approximation and l2,1-norm minimization,
IVC(57), No. 1, 2017, pp. 67-77.
Elsevier DOI 1702
Background: low rank; objects: sparse. BibRef

Weinberg, G.V.,
An Invariant Sliding Window Detection Process,
SPLetters(24), No. 7, July 2017, pp. 1093-1097.
IEEE DOI 1706
Adaptation models, Clutter, Detectors, Radar signal processing, Random variables, Shape, Surveillance, Constant false alarm rate (CFAR), invariance, radar detection, scale and power distributions, sliding window detector BibRef

Prakash, T.[Tanmay], Kak, A.C.[Avinash C.],
Active learning for designing detectors for infrequently occurring objects in wide-area satellite imagery,
CVIU(170), 2018, pp. 92-108.
Elsevier DOI 1806
Object detection, Satellite imagery, Active learning, Distributed computing, Feature selection BibRef

Wu, X.[Xin], Hong, D.F.[Dan-Feng], Ghamisi, P.[Pedram], Li, W.[Wei], Tao, R.[Ran],
MsRi-CCF: Multi-Scale and Rotation-Insensitive Convolutional Channel Features for Geospatial Object Detection,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Li, Y.[Yan], Zhang, J.[Junge], Huang, K.Q.[Kai-Qi], Zhang, J.G.[Jian-Guo],
Mixed Supervised Object Detection with Robust Objectness Transfer,
PAMI(41), No. 3, March 2019, pp. 639-653.
IEEE DOI 1902
Detectors, Cats, Robustness, Object detection, Semantics, Training, Face, Weakly supervised detection, mixed supervised detection, robust objectness transfer BibRef

Wu, X.[Xing], Zhang, X.[Xia], Wang, N.[Nan], Cen, Y.[Yi],
Joint Sparse and Low-Rank Multi-Task Learning with Extended Multi-Attribute Profile for Hyperspectral Target Detection,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Wan, F.[Fang], Wei, P.X.[Peng-Xu], Han, Z.J.[Zhen-Jun], Jiao, J.B.[Jian-Bin], Ye, Q.X.[Qi-Xiang],
Min-Entropy Latent Model for Weakly Supervised Object Detection,
PAMI(41), No. 10, October 2019, pp. 2395-2409.
IEEE DOI 1909
BibRef
Earlier: A1, A2, A4, A3, A5: CVPR18(1297-1306)
IEEE DOI 1812
Proposals, Detectors, Object detection, Optimization, Entropy, Redundancy, Task analysis, Weakly supervised learning, recurrent learning. Entropy, Training, Graphical models BibRef

Liu, X.Y.[Xin-Yu], Li, D.H.[Dong-Hui], Dong, N.[Na], Ip, W.H.[Wai Hung], Yung, K.L.[Kai Leung],
Noncooperative Target Detection of Spacecraft Objects Based on Artificial Bee Colony Algorithm,
IEEE_Int_Sys(34), No. 4, July 2019, pp. 3-15.
IEEE DOI 1909
Optimization, Artificial bee colony algorithm, Intelligent systems, Heuristic algorithms, Object detection, Mathematical model BibRef

Chen, C., Ling, Q.,
Adaptive Convolution for Object Detection,
MultMed(21), No. 12, December 2019, pp. 3205-3217.
IEEE DOI 1912
Feature extraction, Detectors, Convolution, Object detection, Adaptive systems, Task analysis, Semantics, object detection, deep learning BibRef

Rahman, M.M., Tan, Y., Xue, J., Lu, K.,
Recent Advances in 3D Object Detection in the Era of Deep Neural Networks: A Survey,
IP(29), 2020, pp. 2947-2962.
IEEE DOI 2002
Survey, Objetc Detection. Object detection, Cameras, Sensors, Laser radar, Task analysis, deep learning BibRef

Li, C.L.[Chuan-Long], Sun, X.M.[Xing-Ming], Zhou, Z.L.[Zhi-Li], Yang, Y.M.[Yi-Min],
Real-time image carrier generation based on generative adversarial network and fast object detection,
RealTimeIP(17), No. 3, June 2020, pp. 655-665.
Springer DOI 2006
BibRef

Zhang, R., Huang, Y., Pu, M., Zhang, J., Guan, Q., Zou, Q., Ling, H.,
Object Discovery From a Single Unlabeled Image by Mining Frequent Itemsets With Multi-Scale Features,
IP(29), 2020, pp. 8606-8621.
IEEE DOI 2009
Feature extraction, Annotations, Saliency detection, Training, Data mining, Task analysis, Semantics, Object discovery, convolutional neural networks BibRef

Hsu, C.C.[Cheng-Chun], Tsai, Y.H.[Yi-Hsuan], Lin, Y.Y.[Yen-Yu], Yang, M.H.[Ming-Hsuan],
Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector,
ECCV20(IX:733-748).
Springer DOI 2011
BibRef

García-Domínguez, M.[Manuel], Domínguez, C.[César], Heras, J.[Jónathan], Mata, E.[Eloy], Pascual, V.[Vico],
UFOD: An AutoML framework for the construction, comparison, and combination of object detection models,
PRL(145), 2021, pp. 135-140.
Elsevier DOI 2104
AutoML, Deep learning, Object detection, Transfer learning BibRef

Wang, H.S.[Hong-Song], Liao, S.C.[Sheng-Cai], Shao, L.[Ling],
AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection,
IP(30), 2021, pp. 4046-4056.
IEEE DOI 2104
Training, Object detection, Feature extraction, Detectors, Generators, Semantics, Proposals, Object detection, unsupervised domain adaptation BibRef

Guo, Y.G.[Ya-Guang], Zou, Q.[Qi], Jin, L.[Lu],
A coarse to fine network for fast and accurate object detection in high-resolution images,
IET-CV(15), No. 4, 2021, pp. 274-282.
DOI Link 2106
BibRef

Fang, X.[Xian], Kuang, Z.S.[Zeng-Sheng], Zhang, R.X.[Rui-Xun], Shao, X.L.[Xiu-Li], Wang, H.P.[Hong-Peng],
Collaborative learning in bounding box regression for object detection,
PRL(148), 2021, pp. 121-127.
Elsevier DOI 2107
Object detection, Bounding box regression, One-stage detector, Loss function, Non-maximum suppression BibRef

Oksuz, K.[Kemal], Cam, B.C.[Baris Can], Kalkan, S.[Sinan], Akbas, E.[Emre],
Imbalance Problems in Object Detection: A Review,
PAMI(43), No. 10, October 2021, pp. 3388-3415.
IEEE DOI 2109
Survey, Object Detection. BibRef
Earlier: A1, A2, A4, A3:
Generating Positive Bounding Boxes for Balanced Training of Object Detectors,
WACV20(883-892)
IEEE DOI 2006
Object detection, Taxonomy, Feature extraction, Deep learning, Pipelines, Neural networks, Pattern analysis, Object detection, objective imbalance. Generators, Detectors, Training, Object detection, Sampling methods, Pipelines, Proposals BibRef

Nie, J.[Jing], Pang, Y.W.[Yan-Wei], Zhao, S.J.[Sheng-Jie], Han, J.G.[Jun-Gong], Li, X.L.[Xue-Long],
Efficient Selective Context Network for Accurate Object Detection,
CirSysVideo(31), No. 9, September 2021, pp. 3456-3468.
IEEE DOI 2109
Feature extraction, Detectors, Object detection, Semantics, Data mining, attention mechanism BibRef

Kim, J.U.[Jung Uk], Kim, S.T.[Seong Tae], Lee, H.J.[Hong Joo], Lee, S.[Sangmin], Ro, Y.M.[Yong Man],
CUA Loss: Class Uncertainty-Aware Gradient Modulation for Robust Object Detection,
CirSysVideo(31), No. 9, September 2021, pp. 3529-3543.
IEEE DOI 2109
Detectors, Uncertainty, Training, Object detection, Automobiles, Feature extraction, Task analysis, Loss gradient modulation, two-stage region-based object detection BibRef

Wang, K.P.[Kun-Peng], Cai, J.X.[Jing-Xiang], Yao, J.[Juan], Liu, P.[Peng], Zhu, Z.Q.[Zhi-Qin],
Co-teaching based pseudo label refinery for cross-domain object detection,
IET-IPR(15), No. 13, 2021, pp. 3189-3199.
DOI Link 2110
BibRef

Chen, Z.[Ze], Fu, Z.H.[Zhi-Hang], Huang, J.Q.[Jian-Qiang], Tao, M.Y.[Ming-Yuan], Jiang, R.X.[Rong-Xin], Tian, X.[Xiang], Chen, Y.W.[Yao-Wu], Hua, X.S.[Xian-Sheng],
Spatial likelihood voting with self-knowledge distillation for weakly supervised object detection,
IVC(116), 2021, pp. 104314.
Elsevier DOI 2112
BibRef
Earlier: A1, A2, A5, A7, A8, Only:
SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection,
CVPR20(12992-13001)
IEEE DOI 2008
Object detection, Weak supervision, Spatial likelihood voting, Self-knowledge distillation. Proposals, Training, Detectors, Task analysis, Feature extraction BibRef

Wang, X.D.[Xiao-Dong], Zeng, X.X.[Xian-Xian], Zhang, Y.[Yun], Chen, K.[Kairui], Li, D.[Dong],
Improved fine-grained object retrieval with Hard Global Softmin Loss objective,
SP:IC(100), 2022, pp. 116515.
Elsevier DOI 2112
Fine-grained object retrieval, Hard Global Softmin Loss, Convolutional neural network BibRef

Chen, S.[Suting], Cheng, Z.[Zehua], Zhang, L.C.[Liang-Chen], Zheng, Y.J.[Yu-Jie],
SnipeDet: Attention-guided pyramidal prediction kernels for generic object detection,
PRL(152), 2021, pp. 302-310.
Elsevier DOI 2112
Attention mechanism, Hard negative mining, Feature enhancement, Object detection, Prediction module BibRef

Chen, C.L.[Chun-Lin], Yu, J.[Jun], Ling, Q.[Qiang],
Sparse attention block: Aggregating contextual information for object detection,
PR(124), 2022, pp. 108418.
Elsevier DOI 2203
Context around objects. Object detection, Self-attention, Convolution neural network BibRef

Li, X.W.[Xue-Wei], Yi, S.[Song], Zhang, R.X.[Rui-Xuan], Fu, X.Z.[Xu-Zhou], Jiang, H.[Han], Wang, C.H.[Chen-Han], Liu, Z.Q.[Zhi-Qiang], Gao, J.[Jie], Yu, J.[Jian], Yu, M.[Mei], Yu, R.G.[Rui-Guo],
Dynamic Sample Weighting for Weakly Supervised Object Detection,
IVC(122), 2022, pp. 104444.
Elsevier DOI 2205
Weakly supervised learning, Object detection, Dynamic sample weighting, Multiple instance learning BibRef

He, Z.W.[Zhen-Wei], Zhang, L.[Lei], Yang, Y.[Yi], Gao, X.B.[Xin-Bo],
Partial Alignment for Object Detection in the Wild,
CirSysVideo(32), No. 8, August 2022, pp. 5238-5251.
IEEE DOI 2208
Detectors, Object detection, Training, Feature extraction, Task analysis, Adaptation models, Upper bound, deep learning BibRef

Han, B.[Bo], He, L.[Lihuo], Yu, Y.[Ying], Lu, W.[Wen], Gao, X.B.[Xin-Bo],
General Deformable RoI Pooling and Semi-Decoupled Head for Object Detection,
MultMed(26), 2024, pp. 9410-9422.
IEEE DOI 2410
Feature extraction, Location awareness, Detectors, Task analysis, Head, Proposals, Neck, Object detection, inconsistent accuracy, semi-decoupled head BibRef

Tang, X.L.[Xian-Lun], Yang, Q.[Qiao], Xiong, D.[Deyi], Xie, Y.[Ying], Wang, H.M.[Hui-Ming], Li, R.[Rui],
Improving Multiscale Object Detection With Off-Centered Semantics Refinement,
CirSysVideo(32), No. 10, October 2022, pp. 6888-6899.
IEEE DOI 2210
Feature extraction, Semantics, Detectors, Convolution, Object detection, Visualization, Task analysis, receptive field BibRef

Tang, X.L.[Xian-Lun], Yang, Q.[Qiao], Zhang, X.[Xi], Deng, W.[Wuquan], Wang, H.M.[Hui-Ming], Gao, X.B.[Xin-Bo],
A Refinement Method for Single-Stage Object Detection Based on Progressive Decoupled Task Alignment,
CirSysVideo(34), No. 5, May 2024, pp. 3383-3394.
IEEE DOI 2405
Task analysis, Location awareness, Feature extraction, Training, Detectors, Semantics, Probabilistic logic, information interaction BibRef

Ruan, Z.L.[Zhong-Ling], Cao, J.Z.[Jian-Zhong], Wang, H.[Hao], Guo, H.[Huinan], Yang, X.[Xin],
Adaptive feedback connection with a single-level feature for object detection,
IET-CV(16), No. 8, 2022, pp. 736-746.
DOI Link 2210
BibRef

Gu, Y.X.[Yong-Xiang], Qin, X.L.[Xiao-Lin], Peng, Y.C.[Yun-Cong], Li, L.[Lu],
Content-Augmented Feature Pyramid Network with Light Linear Spatial Transformers for Object Detection,
IET-IPR(16), No. 13, 2022, pp. 3567-3578.
DOI Link 2210
BibRef

Joseph, K.J., Rajasegaran, J.[Jathushan], Khan, S.[Salman], Khan, F.S.[Fahad Shahbaz], Balasubramanian, V.N.[Vineeth N.],
Incremental Object Detection via Meta-Learning,
PAMI(44), No. 12, December 2022, pp. 9209-9216.
IEEE DOI 2212
Task analysis, Detectors, Object detection, Training, Proposals, Standards, Feature extraction, Object detection, gradient preconditioning BibRef

Khindkar, V.[Vaishnavi], Arora, C.[Chetan], Balasubramanian, V.N.[Vineeth N.], Subramanian, A.[Anbumani], Saluja, R.[Rohit], Jawahar, C.V.,
To miss-attend is to misalign! Residual Self-Attentive Feature Alignment for Adapting Object Detectors,
WACV22(376-386)
IEEE DOI 2202
Visualization, Pipelines, Object detection, Detectors, Benchmark testing, Feature extraction, Transfer, Vision Systems and Applications BibRef

Liu, H.[He], You, X.T.[Xiu-Ting], Wang, T.[Tao], Li, Y.D.[Yi-Dong],
Object detection via inner-inter relational reasoning network,
IVC(130), 2023, pp. 104615.
Elsevier DOI 2301
Object detection, Relational reasoning, Attention model BibRef

Zhen, P.N.[Pei-Ning], Yan, X.T.[Xiao-Tao], Wang, W.[Wei], Hou, T.S.[Tian-Shu], Wei, H.[Hao], Chen, H.B.[Hai-Bao],
Toward Compact Transformers for End-to-End Object Detection With Decomposed Chain Tensor Structure,
CirSysVideo(33), No. 2, February 2023, pp. 872-885.
IEEE DOI 2302
Transformers, Tensors, Computational modeling, Training, Object detection, Quantization (signal), Pipelines, model compression BibRef

Zhang, Y.Q.[Yong-Qiang], Zhang, Y.[Yin], Tian, R.[Rui], Zhang, Z.[Zian], Bai, Y.C.[Yan-Cheng], Zuo, W.M.[Wang-Meng], Ding, M.L.[Ming-Li],
ThumbDet: One thumbnail image is enough for object detection,
PR(138), 2023, pp. 109424.
Elsevier DOI 2303
Object detection, Down-sampling network, Knowledge distillation BibRef

Dong, N.[Na], Zhang, Y.Q.[Yong-Qiang], Ding, M.L.[Ming-Li], Lee, G.H.[Gim Hee],
Towards Non Co-occurrence Incremental Object Detection with Unlabeled In-the-Wild Data,
IJCV(132), No. 11, November 2024, pp. 5066-5083.
Springer DOI 2411
BibRef

Zhang, Y.Q.[Yong-Qiang], Bai, Y.C.[Yan-Cheng], Ding, M.L.[Ming-Li], Li, Y.Q.[Yong-Qiang], Ghanem, B.[Bernard],
W2F: A Weakly-Supervised to Fully-Supervised Framework for Object Detection,
CVPR18(928-936)
IEEE DOI 1812
Detectors, Object detection, Training, Proposals, Streaming media, Cats BibRef

Dong, N.[Na], Zhang, Y.Q.[Yong-Qiang], Ding, M.L.[Ming-Li], Bai, Y.C.[Yan-Cheng],
Class-incremental object detection,
PR(139), 2023, pp. 109488.
Elsevier DOI 2304
Class-incremental learning, Object detection, Information asymmetry, Non-affection distillation, Deep learning BibRef

Wang, C.X.[Chuan-Xu], Wang, H.R.[Hui-Ru],
Cascaded Feature Fusion with Multi-Level Self-Attention Mechanism for Object Detection,
PR(138), 2023, pp. 109377.
Elsevier DOI 2303
Cascaded feature fusion, Multi-level self-attention mechanism, Space-channel feature correlation, Object detection BibRef

Zou, Z.X.[Zheng-Xia], Chen, K.[Keyan], Shi, Z.W.[Zhen-Wei], Guo, Y.H.[Yu-Hong], Ye, J.P.[Jie-Ping],
Object Detection in 20 Years: A Survey,
PIEEE(111), No. 3, March 2023, pp. 257-276.
IEEE DOI 2303
Survey, Object Detection. Object detection, Detectors, Feature extraction, Deep learning, Convolutional neural networks, technical evolution BibRef

Wang, B.Y.[Bo-Ying], Ji, R.[Ruyi], Zhang, L.[Libo], Wu, Y.J.[Yan-Jun],
Bridging Multi-Scale Context-Aware Representation for Object Detection,
CirSysVideo(33), No. 5, May 2023, pp. 2317-2329.
IEEE DOI 2305
Feature extraction, Semantics, Object detection, Head, Detectors, Proposals, Task analysis, Deep learning, object detection, context-aware BibRef

Li, X.[Xuexue], Diao, W.H.[Wen-Hui], Mao, Y.Q.[Yong-Qiang], Gao, P.[Peng], Mao, X.[Xiuhua], Li, X.M.[Xin-Ming], Sun, X.[Xian],
OGMN: Occlusion-guided multi-task network for object detection in UAV images,
PandRS(199), 2023, pp. 242-257.
Elsevier DOI 2305
Object detection, UAV image, Multi-task learning, Occlusion localization, Multi-task interaction BibRef

Zhang, L.[Luming], Wang, G.[Guifeng], Chen, M.[Ming], Ren, F.[Fuji], Shao, L.[Ling],
An enhanced noise-tolerant hashing for drone object detection,
PR(143), 2023, pp. 109762.
Elsevier DOI 2310
Multiple attributes, Attributes fusion, Noise-tolerant, Deep hashing, Drone, Matrix factorization BibRef

You, S.[Shuai], Xie, X.D.[Xue-Dong], Feng, Y.J.[Yu-Jian], Mei, C.J.[Chao-Jun], Ji, Y.[Yimu],
Multi-Scale Aggregation Transformers for Multispectral Object Detection,
SPLetters(30), 2023, pp. 1172-1176.
IEEE DOI 2310
BibRef

Liu, W.B.[Wen-Bing], Wang, H.B.[Hai-Bo], Gao, Q.X.[Quan-Xue], Zhu, Z.R.[Zhao-Rui],
Multi-modal object detection via transformer network,
IET-IPR(17), No. 12, 2023, pp. 3541-3550.
DOI Link 2310
image representations, object detection BibRef

Xu, J.T.[Jing-Tao], Li, Y.L.[Ya-Li], Wang, S.J.[Sheng-Jin],
AdaZoom: Towards Scale-Aware Large Scene Object Detection,
MultMed(25), 2023, pp. 4598-4609.
IEEE DOI 2310
BibRef

Li, Y.L.[Ya-Li], He, F.[Fei], Lu, W.H.[Wen-Hao], Wang, S.J.[Sheng-Jin],
Combining Fast Extracted Edge Descriptors and Feature Sharing for Rapid Object Detection,
DTCE12(II:478-490).
Springer DOI 1304
BibRef

Shen, J.F.[Ji-Feng], Chen, Y.F.[Yi-Fei], Liu, Y.[Yue], Zuo, X.[Xin], Fan, H.[Heng], Yang, W.K.[Wan-Kou],
ICAFusion: Iterative cross-attention guided feature fusion for multispectral object detection,
PR(145), 2024, pp. 109913.
Elsevier DOI Code:
WWW Link. 2311
Multispectral object detection, Cross-attention, Transformer, Iterative feature fusion BibRef

Lin, W.J.[Wen-Jie], Chu, J.[Jun], Leng, L.[Lu], Miao, J.[Jun], Wang, L.F.[Ling-Feng],
Feature disentanglement in one-stage object detection,
PR(145), 2024, pp. 109878.
Elsevier DOI 2311
Object detection, Feature misalignment, Response alignment, Feature disentanglement, Soft sampling BibRef

Zhou, Q.[Qiang], Yu, C.H.[Chao-Hui],
Object Detection Made Simpler by Eliminating Heuristic NMS,
MultMed(25), 2023, pp. 9254-9262.
IEEE DOI 2312
non-maximum suppression. BibRef

Shao, M.W.[Ming-Wen], Peng, Z.[Zilu],
Distance metric-based learning for long-tail object detection,
IVC(142), 2024, pp. 104888.
Elsevier DOI 2402
Deep convolutional neural network, Object detection, Long-tail distribution, Metric learning, Feature extraction BibRef

Qi, T.H.[Tian-Hao], Xie, H.T.[Hong-Tao], Li, P.[Pandeng], Ge, J.N.[Jian-Nan], Zhang, Y.D.[Yong-Dong],
Balanced Classification: A Unified Framework for Long-Tailed Object Detection,
MultMed(26), 2024, pp. 3088-3101.
IEEE DOI 2402
Tail, Detectors, Training, Object detection, Feature extraction, Head, Task analysis, Long-tailed object detection, Feature hallucination module BibRef

Jiang, Z.T.[Ze-Tao], Huang, Q.Y.[Qin-Yang], Zhang, H.J.[Hui-Juan],
Channel-level Matching Knowledge Distillation for object detectors via MSE,
PRL(179), 2024, pp. 52-57.
Elsevier DOI 2403
Knowledge distillation, Object detection, Channel matching, Mean squared error BibRef

Lee, S.[Seungik], Park, J.[Jaehyeong], Park, J.[Jinsun],
CrossFormer: Cross-guided attention for multi-modal object detection,
PRL(179), 2024, pp. 144-150.
Elsevier DOI 2403
Object detection, Multi-modal, Sensor fusion BibRef

Hua, J.[Jie], Wang, Z.Y.[Zhong-Yuan], Zou, Q.[Qin], Xiao, J.S.[Jin-Sheng], Tian, X.[Xin], Zhang, Y.F.[Yu-Fei],
Re-decoupling the classification branch in object detectors for few-class scenes,
PR(153), 2024, pp. 110541.
Elsevier DOI 2405
Feature degradation, Re-decoupling, Few-class scenes, Object detection, Mutual exclusion constraint BibRef

Cheng, J.[Jikang], Huang, B.J.[Bao-Jin], Fang, Y.[Yan], Han, Z.[Zhen], Wang, Z.Y.[Zhong-Yuan],
Adversarial intensity awareness for robust object detection,
CVIU(251), 2025, pp. 104252.
Elsevier DOI 2501
Object detection, Adversarial training, Adversarial attack, Model robustness BibRef

Fauzi, N.I.H.[Nurul Izzatie Husna], Musa, Z.[Zalili], Hujainah, F.[Fadhl],
Feature-Based Object Detection and Tracking: A Systematic Literature Review,
IJIG(24), No. 3, May 2024, pp. 2450037.
DOI Link 2406
Survey, Object Detection. BibRef

Zhao, L.[Liang], Teng, Y.[Yao], Wang, L.M.[Li-Min],
Logit Normalization for Long-Tail Object Detection,
IJCV(132), No. 6, June 2024, pp. 2114-2134.
Springer DOI 2406
BibRef

Teng, Y.[Yao], Liu, H.S.[Hai-Song], Guo, S.[Sheng], Wang, L.M.[Li-Min],
StageInteractor: Query-based Object Detector with Cross-stage Interaction,
ICCV23(6554-6565)
IEEE DOI Code:
WWW Link. 2401
BibRef

Li, H.C.[Hao-Chen], Zhang, R.[Rui], Yao, H.T.[Han-Tao], Zhang, X.[Xin], Hao, Y.F.[Yi-Fan], Song, X.K.[Xin-Kai], Li, L.[Ling],
REACT: Remainder Adaptive Compensation for Domain Adaptive Object Detection,
IP(33), 2024, pp. 3735-3748.
IEEE DOI 2406
Feature extraction, Task analysis, Data mining, Detectors, Object detection, Image edge detection, Annotations, prototypes BibRef

Wang, Y.[Yu], Zhang, R.[Rui], Zhang, S.[Shuo], Li, M.[Miao], Xia, Y.Y.[Yang-Yang], Zhang, X.S.[Xi-Shan], Liu, S.L.[Shao-Li],
Domain-Specific Suppression for Adaptive Object Detection,
CVPR21(9598-9607)
IEEE DOI 2111
Degradation, Adaptation models, Convolution, Semantics, Object detection, Feature extraction BibRef

Sunanda, P.[Perla], Kavitha, D.[Dwaram],
Generic object detection in real-time images under poorly visible conditions: a systematic literature review,
IJCVR(14), No. 4, 2024, pp. 401-444.
DOI Link 2407
Survey, Object Detection. BibRef

Zhao, Z.Y.[Zheng-Yang], Wang, B.[Buhong], Wang, Z.[Zhen], Yao, X.[Xuan],
DBI-Attack: Dynamic Bi-Level Integrated Attack for Intensive Multi-Scale UAV Object Detection,
RS(16), No. 14, 2024, pp. 2570.
DOI Link 2408
BibRef

Deng, J.H.[Jin-Hong], Li, W.[Wen], Duan, L.X.[Li-Xin],
Balanced Teacher for Source-Free Object Detection,
CirSysVideo(34), No. 8, August 2024, pp. 7231-7243.
IEEE DOI 2408
Object detection, Training, Detectors, Adaptation models, Uncertainty, Data models, Minimization, Object detection, self-training BibRef

Deng, J.H.[Jin-Hong], Xu, D.L.[Dong-Li], Li, W.[Wen], Duan, L.X.[Li-Xin],
Harmonious Teacher for Cross-Domain Object Detection,
CVPR23(23829-23838)
IEEE DOI 2309
BibRef

Deng, J.H.[Jin-Hong], Li, W.[Wen], Chen, Y.H.[Yu-Hua], Duan, L.X.[Li-Xin],
Unbiased Mean Teacher for Cross-domain Object Detection,
CVPR21(4089-4099)
IEEE DOI 2111
Training, Adaptation models, Computational modeling, 3G mobile communication, Estimation, Object detection BibRef

Yu, W.W.[Wan-Wan], Zhang, J.P.[Jun-Ping], Liu, D.Y.[Dong-Yang], Xi, Y.Q.[Yun-Qiao], Wu, Y.[Yinhu],
An Effective and Lightweight Full-Scale Target Detection Network for UAV Images Based on Deformable Convolutions and Multi-Scale Contextual Feature Optimization,
RS(16), No. 16, 2024, pp. 2944.
DOI Link 2408
Wide range of scale for objects. BibRef

Lee, H.[Hojun], Kim, S.Y.[Su-Young], Lee, J.[Junhoo], Yoo, J.[Jaeyoung], Kwak, N.[Nojun],
Coreset Selection for Object Detection,
Distill24(7682-7691)
IEEE DOI 2410
Object detection, Vectors, Optimization, Image classification, coreset selection, object detection BibRef

Zheng, Z.H.[Zhao-Hui], Chen, Y.M.[Yu-Ming], Hou, Q.[Qibin], Li, X.[Xiang], Wang, P.[Ping], Cheng, M.M.[Ming-Ming],
Zone Evaluation: Revealing Spatial Bias in Object Detection,
PAMI(46), No. 12, December 2024, pp. 8636-8651.
IEEE DOI 2411
I.e. detectors fail near image borders. Detectors, Object detection, Cows, Birds, Training, Robustness, Dogs, Object detection, zone evaluation, spatial bias, spatial equilibrium learning BibRef

Wang, J.B.[Jia-Bao], Chen, Y.M.[Yu-Ming], Zheng, Z.H.[Zhao-Hui], Li, X.[Xiang], Cheng, M.M.[Ming-Ming], Hou, Q.[Qibin],
CrossKD: Cross-Head Knowledge Distillation for Object Detection,
CVPR24(16520-16530)
IEEE DOI 2410
Training, Schedules, Annotations, Object detection, Detectors, Feature extraction, Knowledge distillation, object detection BibRef

Zuo, F.Y.[Feng-Yuan], Liu, J.H.[Jin-Hai], Chen, Z.L.[Zhao-Lin], Zhang, H.G.[Hua-Guang], Fu, M.R.[Ming-Rui], Wang, L.[Lei],
Multilevel Fine-Grained Features-Based General Framework for Object Detection,
Cyber(54), No. 11, November 2024, pp. 6921-6933.
IEEE DOI 2411
Feature extraction, Task analysis, Detectors, Object detection, Accuracy, Semantics, Location awareness, task specific prediction network (TSPN) BibRef

Guo, R.Z.[Run-Ze], Guo, X.J.[Xiao-Jun], Sun, X.Y.[Xiao-Yong], Zhou, P.[Peida], Sun, B.[Bei], Su, S.J.[Shao-Jing],
Background-Aware Cross-Attention Multiscale Fusion for Multispectral Object Detection,
RS(16), No. 21, 2024, pp. 4034.
DOI Link 2411
BibRef

Hu, S.[Sijie], Bonardi, F.[Fabien], Bouchafa, S.[Samia], Prendinger, H.[Helmut], Sidibé, D.[Désiré],
Rethinking Self-Attention for Multispectral Object Detection,
ITS(25), No. 11, November 2024, pp. 16300-16311.
IEEE DOI Code:
WWW Link. 2411
Object detection, Feature extraction, Detectors, Task analysis, Complexity theory, YOLO, Robustness, Multispectral, attention, deep learning BibRef

Li, W.X.[Wen-Xi], Guo, Y.C.[Yu-Chen], Zheng, J.[Jilai], Lin, H.Z.[Hao-Zhe], Ma, C.[Chao], Fang, L.[Lu], Yang, X.K.[Xiao-Kang],
Bridging the gap between object detection in close-up and high-resolution wide shots,
CVIU(249), 2024, pp. 104181.
Elsevier DOI Code:
WWW Link. 2412
Object detection, Super high-resolution, Wide shots BibRef

Qian, Y.[Yu], Wang, Q.J.[Qi-Jin], Wu, C.X.[Chang-Xin], Wang, C.[Chao], Cheng, L.[Long], Hu, Y.T.[Ya-Ting], Wang, H.Q.[Hong-Qiang],
Apply prior feature integration to sparse object detectors,
PR(159), 2025, pp. 111103.
Elsevier DOI 2412
Object detection, Deep learning, Feature extraction, Sparse object candidates BibRef

Hu, Z.L.[Zhi-Liang], Chen, S.[Si], Hua, Y.[Yang], Wang, D.H.[Da-Han], Zhu, S.Z.[Shun-Zhi], Yan, Y.[Yan],
DHLA: Dynamic Hybrid Label Assignment for End-to-End Object Detection,
CirSysVideo(35), No. 2, February 2025, pp. 1055-1069.
IEEE DOI 2502
Training, Detectors, Object detection, Feature extraction, Transformers, Silicon, dynamic soft label BibRef

Ye, K.[Kai], Huang, Z.P.[Ze-Peng], Xiong, Y.L.[Yi-Lei], Gao, Y.[Yu], Xie, J.H.[Jin-Heng], Shen, L.L.[Lin-Lin],
Progressive Pseudo Labeling for Multi-Dataset Detection Over Unified Label Space,
MultMed(27), 2025, pp. 531-543.
IEEE DOI 2502
Detectors, Annotations, Labeling, Adaptation models, Training, Faces, Object detection, Filtering, Benchmark testing, Proposals, unified label space BibRef

Huang, X.Q.[Xiao-Qian], Gong, Y.[Yong], Wu, W.H.[Wen-Hao], Zhu, S.[Saike], Zhao, Y.[Yi],
CSDet: A Compressed Sensing Object Detection Architecture With Lightweight Networks,
CirSysVideo(35), No. 3, March 2025, pp. 2355-2368.
IEEE DOI 2503
Image reconstruction, Object detection, Image coding, Feature extraction, Sparse matrices, Computational modeling, image reconstruction BibRef

Zheng, S.D.[Shang-Dong], Wu, Z.B.[Ze-Bin], Xu, Y.[Yang], He, C.X.[Cheng-Xun], Wei, Z.H.[Zhi-Hui],
Detector With Classifier2: An End-to-End Multi-Stream Feature Aggregation Network for Fine-Grained Object Detection in Remote Sensing Images,
IP(34), 2025, pp. 2707-2720.
IEEE DOI Code:
WWW Link. 2505
Object detection, Feature extraction, Semantics, Proposals, Remote sensing, Visualization, Location awareness, Correlation, feature aggregation BibRef

Jin, Y.X.[Yu-Xi], Yin, C.R.[Chao-Ran], Wang, T.Q.[Tian-Qi], Hao, C.P.[Cheng-Peng], Orlando, D.[Danilo],
An Adaptive Target Detection Architecture for Mismatched Signals,
SPLetters(32), 2025, pp. 1860-1864.
IEEE DOI 2505
Detectors, Vectors, Object detection, Bayes methods, Probability density function, radar target detection BibRef

Zhao, J.Q.[Jia-Qi], Ou, Q.F.[Qing-Feng], Zhou, Y.[Yong], Du, W.L.[Wen-Liang], Yao, R.[Rui],
Grid-distance-based selection for fine-grained object detection in aerial images,
PRL(194), 2025, pp. 13-19.
Elsevier DOI 2506
Remote sensing, Object detection, Label assignment, Fine-grained BibRef

Ramos, P.[Patrick], Gonthier, N.[Nicolas], Khan, S.[Selina], Nakashima, Y.[Yuta], Garcia, N.[Noa],
No Annotations for Object Detection in Art Through Stable Diffusion,
WACV25(6228-6237)
IEEE DOI Code:
WWW Link. 2505
Art, Codes, Annotations, Pipelines, Object detection, Detectors, Diffusion models, Object recognition, Painting, object detection, weakly-supervised learning BibRef

Shi, C.[Cheng], Zhu, Y.C.[Yu-Chen], Yang, S.[Sibei],
Plain-det: A Plain Multi-dataset Object Detector,
ECCV24(V: 210-226).
Springer DOI 2412
BibRef

Li, Y.H.[Yu-Hang], Dong, X.[Xin], Chen, C.[Chen], Zhuang, W.M.[Wei-Ming], Lyu, L.J.[Ling-Juan],
A Simple Background Augmentation Method for Object Detection with Diffusion Model,
ECCV24(LXVI: 462-479).
Springer DOI 2412
BibRef

Howlader, P.[Prantik], Le, H.[Hieu], Samaras, D.[Dimitris],
Weighting Pseudo-labels via High-activation Feature Index Similarity and Object Detection for Semi-supervised Segmentation,
ECCV24(LXXV: 456-474).
Springer DOI 2412
BibRef

El Khoury, K.[Karim], Godelaine, T.[Tiffanie], Delvaux, S.[Simon], Lugan, S.[Sébastien], Macq, B.[Benoît],
Streamlined Hybrid Annotation Framework Using Scalable Codestream for Bandwidth-Restricted UAV Object Detection,
ICIP24(1581-1587)
IEEE DOI 2411
Adaptation models, Image resolution, Annotations, Decision making, Transform coding, Object detection, Streaming media, Hybrid Annotation BibRef

Ding, Y.[Yifu], Feng, W.[Weilun], Chen, C.[Chuyan], Guo, J.Y.[Jin-Yang], Liu, X.L.[Xiang-Long],
Reg-PTQ: Regression-specialized Post-training Quantization for Fully Quantized Object Detector,
CVPR24(16174-16184)
IEEE DOI 2410
Performance evaluation, Degradation, Deep learning, Quantization (signal), Image edge detection, Detectors, detection, post-training quantization BibRef

Bao, J.[Jun], Liu, B.[Buyu], Ren, K.[Kui], Yu, J.[Jun],
GLOW: Global Layout Aware Attacks on Object Detection,
CVPR24(12057-12066)
IEEE DOI 2410
Measurement, Reviews, Layout, Semantics, Closed box, Estimation,Object detection BibRef

Helvig, K.[Kevin], Abeloos, B.[Baptiste], Trouvé-Peloux, P.[Pauline],
CAFF-DINO: Multi-spectral object detection transformers with cross-attention features fusion,
PBVS24(3037-3046)
IEEE DOI 2410
Couplings, Visualization, Systematics, Head, Architecture, Object detection, Detectors BibRef

Zhou, C.Y.[Chang-Yuan], Guo, Y.M.[Yu-Min], Lv, Q.[Qinxue], Yuan, J.[Ji],
Optimizing Object Detection via Metric-driven Training Data Selection,
VDU24(7348-7355)
IEEE DOI Code:
WWW Link. 2410
Training, Adaptation models, Refining, Training data, Object detection, Data models BibRef

Luo, X.S.[Xing-Shuang], Cui, Z.[Zhe], Su, F.[Fei],
FE-Det: An Effective Traffic Object Detection Framework for Fish-Eye Cameras,
AICity24(7091-7099)
IEEE DOI 2410
Pedestrians, Shape, Surveillance, Scalability, Urban areas, Object detection, Traffic control, Traffic Object Detection, Static Objects Processing BibRef

Pham, L.H.[Long Hoang], Ho, Q.P.N.[Quoc Pham-Nam], Tran, D.N.N.[Duong Nguyen-Ngoc], Tran, T.H.P.[Tai Huu-Phuong], Nguyen, H.H.[Huy-Hung], Vu, D.K.[Duong Khac], Tran, C.D.[Chi Dai], Huynh, N.D.M.[Ngoc Doan-Minh], Jeon, H.M.[Hyung-Min], Jeon, H.J.[Hyung-Joon], Jeon, J.W.[Jae Wook],
Improving Object Detection to Fisheye Cameras with Open-Vocabulary Pseudo-Label Approach,
AICity24(7100-7107)
IEEE DOI 2410
Training, Adaptation models, Vocabulary, Roads, Urban areas, Object detection, Fisheye camera, object detection, style transfer BibRef

Ofori-Oduro, M.[Mark], Amer, M.[Maria],
Defending Object Detection Models against Image Distortions,
WACV24(3842-3851)
IEEE DOI Code:
WWW Link. 2404
Training, Computational modeling, Estimation, Object detection, Transforms, Distortion, Data augmentation, Algorithms, Image recognition and understanding BibRef

Popordanoska, T.[Teodora], Tiulpin, A.[Aleksei], Blaschko, M.B.[Matthew B.],
Beyond Classification: Definition and Density-based Estimation of Calibration in Object Detection,
WACV24(574-583)
IEEE DOI 2404
Estimation, Object detection, Detectors, Artificial neural networks, Calibration, Task analysis, Algorithms, Biomedical / healthcare / medicine BibRef

Ben Saad, A.[Ahmed], Facciolo, G.[Gabriele], Davy, A.[Axel],
On the Importance of Large Objects in CNN Based Object Detection Algorithms,
WACV24(522-531)
IEEE DOI 2404
Evaluation, Object Detection. Training, Machine learning algorithms, Object detection, Detectors, Feature extraction, Robustness, Algorithms, Datasets and evaluations BibRef

Hou, Y.[Yuguo], Chang, H.W.[Hong-Wei], Mi, W.P.[Wen-Peng], Zhang, Z.Y.[Zheng-Yi], Zhang, L.[Li], Wang, F.Z.[Fu-Zhong],
A Nonnegative Sparse and Collaborative Method for Hyperspectral Target Detection,
CVIDL23(631-640)
IEEE DOI 2403
Face recognition, Signal processing algorithms, Matching pursuit algorithms, Collaboration, Object detection, orthogonal matching pursuit BibRef

Nagase, Y.[Yasuto], Babazaki, Y.[Yasunori], Takahashi, K.[Katsuhiko],
Multi-Plane Projection for Extending Perspective Image Object Detection Models to 360° Images,
MVA23(1-5)
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Training, Surveys, Adaptation models, Image transformation, Image recognition, Machine vision, Object detection BibRef

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Fast Object Detection in High-Resolution Videos,
REDLCV23(1461-1470)
IEEE DOI 2401
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Dong, Y.[Yudi], Yue, X.D.[Xiao-Dong], Xu, Z.K.[Zhi-Kang], Xie, S.R.[Shao-Rong],
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ICIP23(3150-3154)
IEEE DOI 2312
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Mao, J.[Jiafeng], Yu, Q.[Qing], Irie, G.[Go], Aizawa, K.[Kiyoharu],
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ICIP23(1575-1579)
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Ghosh, A.[Anurag], Reddy, N.D.[N. Dinesh], Mertz, C.[Christoph], Narasimhan, S.G.[Srinivasa G.],
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Si, W.W.[Wen-Wen], Li, S.[Shuo], Park, S.[Sangdon], Lee, I.[Insup], Bastani, O.[Osbert],
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CVPR23(24638-24647)
IEEE DOI 2309
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Cao, Y.[Yue], Bin, J.C.[Jun-Chi], Hamari, J.[Jozsef], Blasch, E.[Erik], Liu, Z.[Zheng],
Multimodal Object Detection by Channel Switching and Spatial Attention,
PBVS23(403-411)
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Oksuz, K.[Kemal], Joy, T.[Tom], Dokania, P.K.[Puneet K.],
Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration,
CVPR23(9263-9274)
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Unbalanced Optimal Transport: A Unified Framework for Object Detection,
CVPR23(3198-3207)
IEEE DOI 2309
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Chen, Y.B.[Yan-Bei], Wang, M.[Manchen], Mittal, A.[Abhay], Xu, Z.L.[Zhen-Lin], Favaro, P.[Paolo], Tighe, J.[Joseph], Modolo, D.[Davide],
ScaleDet: A Scalable Multi-Dataset Object Detector,
CVPR23(7288-7297)
IEEE DOI 2309
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Zhang, G.J.[Gong-Jie], Luo, Z.P.[Zhi-Peng], Tian, Z.C.[Zi-Chen], Zhang, J.Y.[Jing-Yi], Zhang, X.Q.[Xiao-Qin], Lu, S.J.[Shi-Jian],
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CVPR23(6206-6216)
IEEE DOI 2309
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Liang, W.T.[Wen-Teng], Xue, F.[Feng], Liu, Y.H.[Yi-Hao], Zhong, G.F.[Guo-Feng], Ming, A.[Anlong],
Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown Objects,
CVPR23(3230-3239)
IEEE DOI 2309
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Li, M.F.[Meng-Fan], Meng, M.[Ming], Zhou, Z.[Zhong],
REPF-Net: Distortion-Aware Re-Projection Fusion Network for Object Detection in Panorama Image,
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Springer DOI 2307
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Isaac-Medina, B.K.S.[Brian K. S.], Willcocks, C.G.[Chris G.], Breckon, T.P.[Toby P.],
Multi-view Vision Transformers for Object Detection,
ICPR22(4678-4684)
IEEE DOI 2212
E.g. multi-view X-Ray security, or pedestrian datasets. Aggregates, Detectors, Object detection, Transformers, Feature extraction BibRef

Kumar, S.A.[S. Arun], Pal, A.[Abhijit], Mopuri, K.R.[Konda Reddy], Gorthi, R.K.[Rama Krishna],
Adv-Cut Paste: Semantic adversarial class specific data augmentation technique for object detection,
ICPR22(3632-3638)
IEEE DOI 2212
Training, Deep learning, Semantics, Object detection, Data models, Adversarial machine learning BibRef

Li, Y.S.[Yun-Sheng], Chen, Y.P.[Yin-Peng], Dai, X.Y.[Xi-Yang], Chen, D.D.[Dong-Dong], Liu, M.C.[Meng-Chen], Yu, P.[Pei], Jin, Y.[Ying], Yuan, L.[Lu], Liu, Z.C.[Zi-Cheng], Vasconcelos, N.M.[Nuno M.],
Should All Proposals Be Treated Equally in Object Detection?,
ECCV22(XXV:556-572).
Springer DOI 2211
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Maaz, M.[Muhammad], Rasheed, H.[Hanoona], Khan, S.[Salman], Khan, F.S.[Fahad Shahbaz], Anwer, R.M.[Rao Muhammad], Yang, M.H.[Ming-Hsuan],
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ECCV22(X:512-531).
Springer DOI 2211
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Hess, G.[Georg], Petersson, C.[Christoffer], Svensson, L.[Lennart],
Object Detection as Probabilistic Set Prediction,
ECCV22(X:550-566).
Springer DOI 2211
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Xu, W.P.[Wei-Peng], Chu, P.Z.[Peng-Zhi], Xie, R.[Renhao], Xiao, X.Z.[Xiong-Ziyan], Huang, H.C.[Hong-Cheng],
Robust and Accurate Object Detection Via Self-Knowledge Distillation,
ICIP22(91-95)
IEEE DOI 2211
Training, Codes, Object detection, Detectors, Self-supervised learning, Benchmark testing, Feature extraction, knowledge distillation BibRef

Yamauchi, T.[Toshinori], Ishikawa, M.[Masayoshi],
Spatial Sensitive GRAD-CAM: Visual Explanations for Object Detection by Incorporating Spatial Sensitivity,
ICIP22(256-260)
IEEE DOI 2211
Visualization, Sensitivity, Computational modeling, Focusing, Detectors, Object detection, Feature extraction, XAI, Grad-CAM BibRef

Cao, M.[Miao], Ikehata, S.[Satoshi], Aizawa, K.[Kiyoharu],
Dual-ERP Representation for Object Detection in 360° Images,
ICIP22(2016-2020)
IEEE DOI 2211
Training, Image recognition, Detectors, Object detection, Distortion, ERP, Object Detection, 360° images BibRef

Jang, Y.[Younho], Shin, W.[Wheemyung], Kim, J.[Jinbeom], Woo, S.[Simon], Bae, S.H.[Sung-Ho],
GLAMD: Global and Local Attention Mask Distillation for Object Detectors,
ECCV22(X:460-476).
Springer DOI 2211
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Otani, M.[Mayu], Togashi, R.[Riku], Nakashima, Y.[Yuta], Rahtu, E.[Esa], Heikkilä, J.[Janne], Satoh, S.[Shin'ichi],
Optimal Correction Cost for Object Detection Evaluation,
CVPR22(21075-21083)
IEEE DOI 2210
Costs, Layout, Transportation, Detectors, Object detection, Datasets and evaluation, retrieval BibRef

Tang, Y.[Yehui], Han, K.[Kai], Guo, J.Y.[Jian-Yuan], Xu, C.[Chang], Li, Y.X.[Yan-Xi], Xu, C.[Chao], Wang, Y.H.[Yun-He],
An Image Patch is a Wave: Phase-Aware Vision MLP,
CVPR22(10925-10934)
IEEE DOI 2210
Phase modulation, Aggregates, Semantics, Object detection, Transformers, Representation learning BibRef

Zhou, X.Y.[Xing-Yi], Koltun, V.[Vladlen], Krähenbühl, P.[Philipp],
Simple Multi-dataset Detection,
CVPR22(7561-7570)
IEEE DOI 2210
Training, Protocols, Taxonomy, Semantics, Detectors, Object detection, Recognition: detection, categorization, retrieval BibRef

Gao, Z.T.[Zi-Teng], Wang, L.M.[Li-Min], Han, B.[Bing], Guo, S.[Sheng],
AdaMixer: A Fast-Converging Query-Based Object Detector,
CVPR22(5354-5363)
IEEE DOI 2210
Training, Navigation, Detectors, Feature extraction, Decoding, Recognition: detection, Deep learning architectures and techniques BibRef

Chen, Y.P.[Yin-Peng], Dai, X.[Xiyang], Chen, D.D.[Dong-Dong], Liu, M.C.[Meng-Chen], Dong, X.Y.[Xiao-Yi], Yuan, L.[Lu], Liu, Z.C.[Zi-Cheng],
Mobile-Former: Bridging MobileNet and Transformer,
CVPR22(5260-5269)
IEEE DOI 2210
Bridges, Object detection, Detectors, Transformers, Encoding, Computational efficiency, Recognition: detection, categorization, Representation learning BibRef

Fan, J.H.[Jia-Hao], Liu, H.[Huabin], Yang, W.J.[Wen-Jie], See, J.[John], Zhang, A.[Aixin], Lin, W.Y.[Wei-Yao],
Speed up Object Detection on Gigapixel-level Images with Patch Arrangement,
CVPR22(4643-4653)
IEEE DOI 2210
Image resolution, Image recognition, Costs, Layout, Object detection, Reinforcement learning, Recognition: detection, categorization, Efficient learning and inferences BibRef

Dai, X.Y.[Xi-Yang], Chen, Y.P.[Yin-Peng], Xiao, B.[Bin], Chen, D.D.[Dong-Dong], Liu, M.C.[Meng-Chen], Yuan, L.[Lu], Zhang, L.[Lei],
Dynamic Head: Unifying Object Detection Heads with Attentions,
CVPR21(7369-7378)
IEEE DOI 2111
Code, Object Detection.
WWW Link. Location awareness, Codes, Computational modeling, Object detection, Detectors, Feature extraction BibRef

Li, S.[Shuai], He, C.H.[Chen-Hang], Li, R.H.[Rui-Huang], Zhang, L.[Lei],
A Dual Weighting Label Assignment Scheme for Object Detection,
CVPR22(9377-9386)
IEEE DOI 2210
Measurement, Training, Schedules, Privacy, Military computing, Detectors, Object detection, Recognition: detection, retrieval BibRef

Miller, D.[Dimity], Goode, G.[Georgia], Bennie, C.[Callum], Moghadam, P.[Peyman], Jurdak, R.[Raja],
Why Object Detectors Fail: Investigating the Influence of the Dataset,
VDU22(4822-4829)
IEEE DOI 2210
Conferences, Detectors, Object detection, Market research BibRef

Murrugarra-Llerena, J.[Jeffri], Kirsten, L.[Ln], Jung, C.R.[Claudio R.],
Can we trust bounding box annotations for object detection?,
VDU22(4812-4821)
IEEE DOI 2210
Degradation, Training, Annotations, Object detection, Detectors, Size measurement BibRef

Cai, L.[Likun], Zhang, Z.[Zhi], Zhu, Y.[Yi], Zhang, L.[Li], Li, M.[Mu], Xue, X.Y.[Xiang-Yang],
BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training,
VDU22(4776-4786)
IEEE DOI 2210
Training, Taxonomy, Training data, Object detection, Detectors BibRef

Liu, Y.Y.[Yuan-Yuan], Liu, Z.Y.[Zi-Yang], Fang, F.[Fang], Fu, Z.H.[Zhang-Hua], Chen, Z.L.[Zhan-Long],
Hierarchical Domain-Consistent Network for Cross-Domain Object Detection,
ICIP21(474-478)
IEEE DOI 2201
Training, Visualization, Convolution, Prototypes, Object detection, Feature extraction, Cross-domain object detection, adversarial learning BibRef

Seib, V.[Viktor], Paulus, D.[Dietrich],
Object Detection in Cluttered Environments with Sparse Keypoint Selection,
TradiCV21(2496-2505)
IEEE DOI 2112
Codes, Neural networks, Robot vision systems, Object detection, Cameras BibRef

VS, V.[Vibashan], Gupta, V.[Vikram], Oza, P.[Poojan], Sindagi, V.A.[Vishwanath A.], Patel, V.M.[Vishal M.],
MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection,
CVPR21(4514-4524)
IEEE DOI 2111
Training, Object detection, Benchmark testing, Feature extraction, Routing BibRef

Wang, T.[Tong], Zhu, Y.S.[You-Song], Zhao, C.Y.[Chao-Yang], Zeng, W.[Wei], Wang, J.Q.[Jin-Qiao], Tang, M.[Ming],
Adaptive Class Suppression Loss for Long-Tail Object Detection,
CVPR21(3102-3111)
IEEE DOI 2111
Training, Adaptation models, Vocabulary, Head, Object detection, Manuals BibRef

Guo, J.Y.[Jian-Yuan], Han, K.[Kai], Wang, Y.H.[Yun-He], Wu, H.[Han], Chen, X.H.[Xing-Hao], Xu, C.J.[Chun-Jing], Xu, C.[Chang],
Distilling Object Detectors via Decoupled Features,
CVPR21(2154-2164)
IEEE DOI 2111
Knowledge engineering, Semantics, Detectors, Object detection, Feature extraction, Neck BibRef

Zhang, S.Y.[Song-Yang], Li, Z.[Zeming], Yan, S.P.[Shi-Peng], He, X.M.[Xu-Ming], Sun, J.[Jian],
Distribution Alignment: A Unified Framework for Long-tail Visual Recognition,
CVPR21(2361-2370)
IEEE DOI 2111
Deep learning, Visualization, Image segmentation, Semantics, Object detection BibRef

Guo, J.Y.[Jian-Yuan], Han, K.[Kai], Wu, H.[Han], Zhang, C.[Chao], Chen, X.H.[Xing-Hao], Xu, C.J.[Chun-Jing], Xu, C.[Chang], Wang, Y.H.[Yun-He],
Positive-Unlabeled Data Purification in the Wild for Object Detection,
CVPR21(2652-2661)
IEEE DOI 2111
Purification, Training data, Image annotation, Object detection, Detectors, Benchmark testing, Semisupervised learning BibRef

Ge, Z.[Zheng], Liu, S.T.[Song-Tao], Li, Z.[Zeming], Yoshie, O.[Osamu], Sun, J.[Jian],
OTA: Optimal Transport Assignment for Object Detection,
CVPR21(303-312)
IEEE DOI 2111

WWW Link. Code, Object Detection. Training, Costs, Codes, Transportation, Estimation, Object detection BibRef

Liu, J.[Ji], Li, D.[Dong], Zheng, R.Z.[Rong-Zhang], Tian, L.[Lu], Shan, Y.[Yi],
RankDetNet: Delving into Ranking Constraints for Object Detection,
CVPR21(264-273)
IEEE DOI 2111
Location awareness, Costs, Object detection, Classification algorithms BibRef

Plaut, E.[Elad], Ben Yaacov, E.[Erez], El Shlomo, B.[Bat],
3D Object Detection from a Single Fisheye Image Without a Single Fisheye Training Image,
OmniCV21(3654-3662)
IEEE DOI 2109
Training, Solid modeling, Training data, Object detection, Detectors, Network architecture BibRef

Pardo, A.[Alejandro], Xu, M.M.[Meng-Meng], Thabet, A.[Ali], Arbeláez, P.[Pablo], Ghanem, B.[Bernard],
BAOD: Budget-Aware Object Detection,
LXCV21(1247-1256)
IEEE DOI 2109
Uncertainty, Annotations, Supervised learning, Diversity reception, Optimization methods, Object detection BibRef

Kim, S.[Songeun], Park, S.Y.[Soon-Yong],
Expandable Spherical Projection and Feature Fusion Methods for Object Detection from Fisheye Images,
MVA21(1-5)
DOI Link 2109
Image edge detection, Object detection, Feature extraction, Cameras, Distortion, Real-time systems BibRef

Jaiswal, A.[Ayush], Wu, Y.[Yue], Natarajan, P.[Pradeep], Natarajan, P.[Premkumar],
Class-agnostic Object Detection,
WACV21(918-927)
IEEE DOI 2106
Training, Visualization, Protocols, Grounding, Object detection BibRef

Fang, F.[Fen], Xu, Q.L.[Qian-Li], Li, L.Y.[Li-Yuan], Gu, Y.[Ying], Lim, J.H.[Joo-Hwee],
Detecting Objects with High Object Region Percentage,
ICPR21(7173-7180)
IEEE DOI 2105
Training, Location awareness, Shape, Costing, Object detection, Detectors, Object-region-percentage, neural network BibRef

Liu, L.Q.[Li-Qiang], Wei, S.A.[Shi-An], Jiang, L.[Long], Wang, Y.T.[Ya-Tao],
Weighted Aggregating Feature Pyramid Network for Object Detection,
CVIDL20(347-353)
IEEE DOI 2102
feature extraction, image representation, object detection, lightweight convolutional module, object detection methods, Object detection BibRef

Bai, Y., Meng, Z.,
Feature Maps Channel Augmentation for Object Detection,
CVIDL20(125-129)
IEEE DOI 2102
object detection, optimisation, optimization solution, inter-channel relationship, Attention Mechanism BibRef

Su, P.[Peng], Wang, K.[Kun], Zeng, X.Y.[Xing-Yu], Tang, S.X.[Shi-Xiang], Chen, D.P.[Da-Peng], Qiu, D.[Di], Wang, X.G.[Xiao-Gang],
Adapting Object Detectors with Conditional Domain Normalization,
ECCV20(XI:403-419).
Springer DOI 2011
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Kim, D.[Dongwan], Tsai, Y.H.[Yi-Hsuan], Suh, Y.[Yumin], Faraki, M.[Masoud], Garg, S.[Sparsh], Chandraker, M.[Manmohan], Han, B.H.[Bo-Hyung],
Learning Semantic Segmentation from Multiple Datasets with Label Shifts,
ECCV22(XXVIII:20-36).
Springer DOI 2211
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Zhao, X.Y.[Xiang-Yun], Schulter, S.[Samuel], Sharma, G.[Gaurav], Tsai, Y.H.[Yi-Hsuan], Chandraker, M.[Manmohan], Wu, Y.[Ying],
Object Detection with a Unified Label Space from Multiple Datasets,
ECCV20(XIV:178-193).
Springer DOI 2011
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Hou, Y.Z.[Yun-Zhong], Zheng, L.[Liang], Gould, S.[Stephen],
Multiview Detection with Feature Perspective Transformation,
ECCV20(VII:1-18).
Springer DOI 2011
Code, Object Detection.
WWW Link. MultiviewX Dataset. BibRef

Carion, N.[Nicolas], Massa, F.[Francisco], Synnaeve, G.[Gabriel], Usunier, N.[Nicolas], Kirillov, A.[Alexander], Zagoruyko, S.[Sergey],
End-to-end Object Detection with Transformers,
ECCV20(I:213-229).
Springer DOI 2011
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Li, J.D.[Jun-De], Ghosh, S.[Swaroop],
Quantum-soft Qubo Suppression for Accurate Object Detection,
ECCV20(XXIX: 158-173).
Springer DOI 2010
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Cao, Y., Chen, K., Loy, C.C., Lin, D.,
Prime Sample Attention in Object Detection,
CVPR20(11580-11588)
IEEE DOI 2008
Detectors, Training, Object detection, Task analysis, Proposals, Measurement, Focusing BibRef

Jiang, C., Xu, H., Zhang, W., Liang, X., Li, Z.,
SP-NAS: Serial-to-Parallel Backbone Search for Object Detection,
CVPR20(11860-11869)
IEEE DOI 2008
Feature extraction, Task analysis, Object detection, Search problems, Neck, Spatial resolution BibRef

Tan, J., Wang, C., Li, B., Li, Q., Ouyang, W., Yin, C., Yan, J.,
Equalization Loss for Long-Tailed Object Recognition,
CVPR20(11659-11668)
IEEE DOI 2008
Training, Task analysis, Proposals, Detectors, Object recognition, Object detection BibRef

Wu, Z., Tao, Q., Lin, G., Cai, J.,
Exploring Bottom-Up and Top-Down Cues With Attentive Learning for Webly Supervised Object Detection,
CVPR20(12933-12942)
IEEE DOI 2008
Object detection, Detectors, Training, Labeling, Task analysis, Feature extraction, Testing BibRef

Wang, X., Zhang, S., Yu, Z., Feng, L., Zhang, W.,
Scale-Equalizing Pyramid Convolution for Object Detection,
CVPR20(13356-13365)
IEEE DOI 2008
Convolution, Feature extraction, Kernel, Detectors, Correlation, Object detection, Head BibRef

Küppers, F., Kronenberger, J., Shantia, A., Haselhoff, A.,
Multivariate Confidence Calibration for Object Detection,
SAIAD20(1322-1330)
IEEE DOI 2008
Calibration, Detectors, Object detection, Logistics, Uncertainty, Task analysis, Standards BibRef

Pato, L.V., Negrinho, R., Aguiar, P.M.Q.,
Seeing without Looking: Contextual Rescoring of Object Detections for AP Maximization,
CVPR20(14598-14606)
IEEE DOI 2008
Detectors, Feature extraction, Visualization, Context modeling, Object detection, Proposals BibRef

Guo, J.Y.[Jian-Yuan], Han, K.[Kai], Wang, Y.H.[Yun-He], Zhang, C.[Chao], Yang, Z.H.[Zhao-Hui], Wu, H.[Han], Chen, X.H.[Xing-Hao], Xu, C.[Chang],
Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection,
CVPR20(11402-11411)
IEEE DOI 2008
Detectors, Neck, Object detection, Feature extraction, Search problems, Task analysis BibRef

Shen, Y., Ji, R., Chen, Z., Hong, X., Zheng, F., Liu, J., Xu, M., Tian, Q.,
Noise-Aware Fully Webly Supervised Object Detection,
CVPR20(11323-11332)
IEEE DOI 2008
Noise measurement, Detectors, Training, Object detection, Task analysis, Data models, Proposals BibRef

Tan, M., Pang, R., Le, Q.V.,
EfficientDet: Scalable and Efficient Object Detection,
CVPR20(10778-10787)
IEEE DOI 2008
Detectors, Feature extraction, Compounds, Object detection, Image resolution, Network architecture, Optimization BibRef

Ramanathan, V., Wang, R., Mahajan, D.,
DLWL: Improving Detection for Lowshot Classes With Weakly Labelled Data,
CVPR20(9339-9349)
IEEE DOI 2008
Proposals, Training, Data models, Object detection, Standards, Predictive models BibRef

Zheng, Y., Huang, D., Liu, S., Wang, Y.,
Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation,
CVPR20(13763-13772)
IEEE DOI 2008
Feature extraction, Subspace constraints, Object detection, Detectors, Task analysis, Semantics, Prototypes BibRef

Qiu, H., Li, H., Wu, Q., Shi, H.,
Offset Bin Classification Network for Accurate Object Detection,
CVPR20(13185-13194)
IEEE DOI 2008
Object detection, Feature extraction, Focusing, Detectors, Explosions, Proposals, Entropy BibRef

Chen, C., Liu, M., Meng, X., Xiao, W., Ju, Q.,
RefineDetLite: A Lightweight One-stage Object Detection Framework for CPU-only Devices,
EDLCV20(2997-3007)
IEEE DOI 2008
Detectors, Training, Feature extraction, Object detection, Convolution, Task analysis, Computational complexity BibRef

Li, H., Wu, Z., Zhu, C., Xiong, C., Socher, R., Davis, L.S.,
Learning From Noisy Anchors for One-Stage Object Detection,
CVPR20(10585-10594)
IEEE DOI 2008
Detectors, Training, Noise measurement, Proposals, Object detection, Standards, Head BibRef

Ramakrishnan, K., Panda, R., Fan, Q., Henning, J., Oliva, A., Feris, R.,
Relationship Matters: Relation Guided Knowledge Transfer for Incremental Learning of Object Detectors,
CLVision20(1009-1018)
IEEE DOI 2008
Proposals, Detectors, Knowledge engineering, Object detection, Training, Task analysis, Knowledge transfer BibRef

Farhadi, M., Ghasemi, M., Vrudhula, S., Yang, Y.,
Enabling Incremental Knowledge Transfer for Object Detection at the Edge,
LPCV20(1591-1599)
IEEE DOI 2008
Adaptation models, Object detection, Computational modeling, Knowledge transfer, Feature extraction, Image edge detection, Performance evaluation BibRef

Li, Y., Pang, Y., Shen, J., Cao, J., Shao, L.,
NETNet: Neighbor Erasing and Transferring Network for Better Single Shot Object Detection,
CVPR20(13346-13355)
IEEE DOI 2008
Feature extraction, Detectors, Object detection, Nanoelectromechanical systems, Logic gates, Semantics BibRef

Chen, C., Zheng, Z., Ding, X., Huang, Y., Dou, Q.,
Harmonizing Transferability and Discriminability for Adapting Object Detectors,
CVPR20(8866-8875)
IEEE DOI 2008
Feature extraction, Training, Object detection, Semantics, Interpolation, Detectors, Task analysis BibRef

Wang, Z., Wu, Z., Lu, J., Zhou, J.,
BiDet: An Efficient Binarized Object Detector,
CVPR20(2046-2055)
IEEE DOI 2008
Detectors, Object detection, Neural networks, Feature extraction, Mutual information, Redundancy, Quantization (signal) BibRef

Srivastava, M.M.[Muktabh Mayank],
Bag of Tricks for Retail Product Image Classification,
ICIAR20(I:71-82).
Springer DOI 2007
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Chapman, N.H.[Nicolas Harvey], Lehnert, C.[Christopher], Browne, W.[Will], Dayoub, F.[Feras],
Enhancing Embodied Object Detection with Spatial Feature Memory,
WACV25(6921-6931)
IEEE DOI 2505
Training, Geometry, Semantics, Noise, Object detection, Detectors, Feature extraction, Robot sensing systems, Reliability, embodied learning BibRef

Hall, D., Dayoub, F., Skinner, J., Zhang, H., Miller, D., Corke, P., Carneiro, G., Angelova, A., Sünderhauf, N.,
Probabilistic Object Detection: Definition and Evaluation,
WACV20(1020-1029)
IEEE DOI 2006
Uncertainty, Object detection, Detectors, Probabilistic logic, Task analysis, Semantics, Robots BibRef

Huang, Z., Ke, W., Huang, D.,
Improving Object Detection with Inverted Attention,
WACV20(1294-1302)
IEEE DOI 2006
Training, Heating systems, Detectors, Feature extraction, Tensile stress, Training data, Object detection BibRef

Yang, Z., Liu, S., Hu, H., Wang, L., Lin, S.,
RepPoints: Point Set Representation for Object Detection,
ICCV19(9656-9665)
IEEE DOI 2004
Code, Object Detection.
WWW Link. object detection, object recognition, point set representation, object detection, modern object detectors, Training BibRef

Li, X.Y.[Xiao-Yan], Kan, M.[Meina], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin],
Weakly Supervised Object Detection With Segmentation Collaboration,
ICCV19(9734-9743)
IEEE DOI 2004
image classification, image representation, image segmentation, learning (artificial intelligence), object detection, Pascal, Image segmentation BibRef

Zhao, Y., Price, B., Cohen, S., Gurari, D.,
Unconstrained Foreground Object Search,
ICCV19(2030-2039)
IEEE DOI 2004
image classification, image retrieval, learning (artificial intelligence), object detection, Image color analysis BibRef

Jiang, P., Hou, Q., Cao, Y., Cheng, M., Wei, Y., Xiong, H.,
Integral Object Mining via Online Attention Accumulation,
ICCV19(2070-2079)
IEEE DOI 2004
Code, Object Detection.
WWW Link. image classification, image segmentation, object detection, object recognition, integral object mining, Benchmark testing BibRef

Li, F., Mo, Z., Wang, P., Liu, Z., Zhang, J., Li, G., Hu, Q., He, X., Leng, C., Zhang, Y., Cheng, J.,
A System-Level Solution for Low-Power Object Detection,
LPCV19(2461-2468)
IEEE DOI 2004
embedded systems, learning (artificial intelligence), object detection, video surveillance, video surveillance, Neural networks BibRef

Shao, S.[Shuai], Li, Z.M.[Ze-Ming], Zhang, T.Y.[Tian-Yuan], Peng, C.[Chao], Yu, G.[Gang], Zhang, X.Y.[Xiang-Yu], Li, J.[Jing], Sun, J.[Jian],
Objects365: A Large-Scale, High-Quality Dataset for Object Detection,
ICCV19(8429-8438)
IEEE DOI 2004
Dataset, Object Detection. feature extraction, image annotation, image classification, image segmentation, learning (artificial intelligence), Clocks BibRef

Wang, T.[Tao], Yuan, L.[Li], Zhang, X.P.[Xiao-Peng], Feng, J.S.[Jia-Shi],
Distilling Object Detectors With Fine-Grained Feature Imitation,
CVPR19(4928-4937).
IEEE DOI 2002
BibRef

Cai, L.[Lile], Zhao, B.[Bin], Wang, Z.[Zhe], Lin, J.[Jie], Foo, C.S.[Chuan Sheng], Aly, M.S.[Mohamed Sabry], Chandrasekhar, V.[Vijay],
MaxpoolNMS: Getting Rid of NMS Bottlenecks in Two-Stage Object Detectors,
CVPR19(9348-9356).
IEEE DOI 2002
BibRef

Arun, A.[Aditya], Jawahar, C.V., Kumar, M.P.[M. Pawan],
Dissimilarity Coefficient Based Weakly Supervised Object Detection,
CVPR19(9424-9433).
IEEE DOI 2002
BibRef

Xu, H.[Hang], Jiang, C.[Chenhan], Liang, X.D.[Xiao-Dan], Li, Z.G.[Zhen-Guo],
Spatial-Aware Graph Relation Network for Large-Scale Object Detection,
CVPR19(9290-9299).
IEEE DOI 2002
BibRef

Lin, D.[Di], Shen, D.G.[Ding-Guo], Shen, S.T.[Si-Ting], Ji, Y.F.[Yuan-Feng], Lischinski, D.[Dani], Cohen-Or, D.[Daniel], Huang, H.[Hui],
ZigZagNet: Fusing Top-Down and Bottom-Up Context for Object Segmentation,
CVPR19(7482-7491).
IEEE DOI 2002
BibRef

Niitani, Y.[Yusuke], Akiba, T.[Takuya], Kerola, T.[Tommi], Ogawa, T.[Toru], Sano, S.[Shotaro], Suzuki, S.[Shuji],
Sampling Techniques for Large-Scale Object Detection From Sparsely Annotated Objects,
CVPR19(6503-6511).
IEEE DOI 2002
BibRef

Barnea, E.[Ehud], Ben-Shahar, O.[Ohad],
Exploring the Bounds of the Utility of Context for Object Detection,
CVPR19(7404-7412).
IEEE DOI 2002
BibRef

Sawatzky, J.[Johann], Souri, Y.[Yaser], Grund, C.[Christian], Gall, J.[Jurgen],
What Object Should I Use? - Task Driven Object Detection,
CVPR19(7597-7606).
IEEE DOI 2002
BibRef

RoyChowdhury, A.[Aruni], Chakrabarty, P.[Prithvijit], Singh, A.[Ashish], Jin, S.[SouYoung], Jiang, H.[Huaizu], Cao, L.L.[Liang-Liang], Learned-Miller, E.G.[Erik G.],
Automatic Adaptation of Object Detectors to New Domains Using Self-Training,
CVPR19(780-790).
IEEE DOI 2002
BibRef

Zhu, X.G.[Xin-Ge], Pang, J.M.[Jiang-Miao], Yang, C.[Ceyuan], Shi, J.P.[Jian-Ping], Lin, D.[Dahua],
Adapting Object Detectors via Selective Cross-Domain Alignment,
CVPR19(687-696).
IEEE DOI 2002
BibRef

Zhou, X.Y.[Xing-Yi], Zhuo, J.C.[Jia-Cheng], Krahenbuhl, P.[Philipp],
Bottom-Up Object Detection by Grouping Extreme and Center Points,
CVPR19(850-859).
IEEE DOI 2002
BibRef

Du, P., Zhang, H., Ma, H.,
Classifier Refinement for Weakly Supervised Object Detection with Class-Specific Activation Map,
ICIP19(3367-3371)
IEEE DOI 1910
Weakly supervised learning, object detection, image-level annotations, class-specific activation map BibRef

Antioquia, A.M.C., Tan, D.S.[D. Stanley], Azcarraga, A., Hua, K.,
Single-Fusion Detector: Towards Faster Multi-Scale Object Detection,
ICIP19(76-80)
IEEE DOI 1910
Object Detection, Feature Fusion, Object Recognition, Convolutional Neural Networks, Deep Learning BibRef

Son, J.[Jeany], Kim, D.[Daniel], Lee, S.[Solae], Kwak, S.[Suha], Cho, M.[Minsu], Han, B.H.[Bo-Hyung],
Forget and Diversify: Regularized Refinement for Weakly Supervised Object Detection,
ACCV18(IV:632-648).
Springer DOI 1906
BibRef

Wever, R.[Rijnder], Runia, T.F.H.[Tom F. H.],
Subitizing with Variational Autoencoders,
BrainDriven18(III:617-627).
Springer DOI 1905
Count number of objects in a small set. BibRef

Mehta, R.[Rakesh], Ozturk, C.[Cemalettin],
Object Detection at 200 Frames per Second,
AutoNUE18(V:659-675).
Springer DOI 1905
BibRef

Joseph, K.J., Patel, R.C.[Rajiv Chunilal], Srivastava, A.[Amit], Gupta, U.[Uma], Balasubramanian, V.N.[Vineeth N.],
MASON: A Model AgnoStic ObjectNess Framework,
AutoNUE18(V:642-658).
Springer DOI 1905
BibRef

Zhang, K.J.[Kai-Jun], Guo, C.H.[Cheng-Hao], Niu, Z.H.[Zhong-Han], Liu, L.F.[Lu-Fei], Yang, Y.B.[Yu-Bin],
SCOD: Dynamical Spatial Constraints for Object Detection,
MMMod19(I:17-28).
Springer DOI 1901
BibRef

Kim, Y.H.[Yong-Hyun], Kang, B.N.[Bong-Nam], Kim, D.J.[Dai-Jin],
Detector with focus: Normalizing gradient in image pyramid,
ICIP17(420-424)
IEEE DOI 1803
Data models, Deformable models, Detectors, Interpolation, Object detection, Pose estimation, Training, detection, gradient, normalization BibRef

Tychsen-Smith, L., Petersson, L.,
DeNet: Scalable Real-Time Object Detection with Directed Sparse Sampling,
ICCV17(428-436)
IEEE DOI 1802
convolution, deconvolution, neural nets, object detection, sampling methods, statistical distributions, BibRef

Chan, J.[Jacob], Lee, J.A.[Jimmy Addison], Kemao, Q.[Qian],
BIND: Binary Integrated Net Descriptors for Texture-Less Object Recognition,
CVPR17(3020-3028)
IEEE DOI 1711
Clutter, Detectors, Encoding, Image edge detection, Object recognition, Resistance, Robustness Compare to BORDER, BOLD, LINE2D BibRef

Chen, K.[Kai], Song, H.[Hang], Loy, C.C.[Chen Change], Lin, D.[Dahua],
Discover and Learn New Objects from Documentaries,
CVPR17(1111-1120)
IEEE DOI 1711
Detectors, Optimization, Pragmatics, Proposals, Training, Visualization BibRef

Hoffman, J.[Judy], Gupta, S.[Saurabh], Darrell, T.J.[Trevor J.],
Learning with Side Information through Modality Hallucination,
CVPR16(826-834)
IEEE DOI 1612
RGB recognition, trained with depth information. BibRef

Shrivastava, A., Gupta, A., Girshick, R.[Ross],
Training Region-Based Object Detectors with Online Hard Example Mining,
CVPR16(761-769)
IEEE DOI 1612
BibRef

Redmon, J., Divvala, S., Girshick, R., Farhadi, A.,
You Only Look Once: Unified, Real-Time Object Detection,
CVPR16(779-788)
IEEE DOI 1612
BibRef

Arrais, R.[Rafael], Oliveira, M.[Miguel], Toscano, C.[César], Veiga, G.[Germano],
A Hybrid Top-Down Bottom-Up Approach for the Detection of Cuboid Shaped Objects,
ICIAR16(512-520).
Springer DOI 1608
BibRef

Duan, K.[Kun], Wang, W.[Wei], Yu, T.[Ting],
Procrustean decomposition for orthogonal cascade detection,
WACV16(1-9)
IEEE DOI 1606
speed up a standard sliding window detector. Detectors BibRef

Newtson, K., Creusere, C.D.,
Histogram Oriented Gradients and Map Seeking Circuits pattern recognition with compressed imagery,
Southwest16(113-116)
IEEE DOI 1605
Feature extraction Finding the edges and correlate the patterns with the object of interest. BibRef

Lu, Y., Lu, C.[Cewu], Tang, C.K.[Chi-Keung],
Online Video Object Detection Using Association LSTM,
ICCV17(2363-2371)
IEEE DOI 1802
object detection, video signal processing, Long Short-Term Memory, association LSTM, Tools BibRef

Lee, M.H.[Man Hee], Park, I.K.[In Kyu],
Performance Evaluation of Local Descriptors for Affine Invariant Region Detector,
RoLoD14(630-643).
Springer DOI 1504
BibRef

Valmadre, J.[Jack], Sridharan, S.[Sridha], Lucey, S.[Simon],
Learning Detectors Quickly with Stationary Statistics,
ACCV14(I: 99-114).
Springer DOI 1504
Object detectors. BibRef

Fang, W.H.[Wen-Hua], Chen, J.[Jun], Liang, C.[Chao], Wang, X.[Xiao], Nan, Y.Y.[Yuan-Yuan], Hu, R.M.[Rui-Min],
Object Detection in Low-Resolution Image via Sparse Representation,
MMMod15(I: 234-245).
Springer DOI 1501
reconstruct higher resolution image for detection. BibRef

Frintrop, S.[Simone], Garcia, G.M.[German Martin], Cremers, A.B.[Armin B.],
A Cognitive Approach for Object Discovery,
ICPR14(2329-2334)
IEEE DOI 1412
Databases BibRef

Ma, K.[Kai], Ben-Arie, J.[Jezekiel],
Compound Exemplar Based Object Detection by Incremental Random Forest,
ICPR14(2407-2412)
IEEE DOI 1412
Dynamic programming BibRef

Riabchenko, E.[Ekaterina], Chen, K.[Ke], Kämäräinen, J.K.[Joni-Kristian],
Progressive Visual Object Detection with Positive Training Examples Only,
SCIA15(388-399).
Springer DOI 1506
BibRef
Earlier: A1, A3, A2:
Density-Aware Part-Based Object Detection with Positive Examples,
ICPR14(2814-2819)
IEEE DOI 1412
Detectors BibRef

Peng, X.C.[Xing-Chao], Saenko, K.[Kate],
Combining Texture and Shape Cues for Object Recognition with Minimal Supervision,
ACCV16(IV: 256-272).
Springer DOI 1704
BibRef

Peng, X.C.[Xing-Chao], Sun, B.C.[Bao-Chen], Ali, K.[Karim], Saenko, K.[Kate],
Learning Deep Object Detectors from 3D Models,
ICCV15(1278-1286)
IEEE DOI 1602
Data models. Use crowdsource 3D CAD models for training. But include low-level cues. BibRef

Sun, B.C.[Bao-Chen], Saenko, K.[Kate],
Deep CORAL: Correlation Alignment for Deep Domain Adaptation,
TASKCV16(III: 443-450).
Springer DOI 1611
BibRef
Earlier:
Subspace Distribution Alignment for Unsupervised Domain Adaptation,
BMVC15(xx-yy).
DOI Link 1601
BibRef
Earlier:
From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Russakovsky, O.[Olga], Deng, J.[Jia], Huang, Z.H.[Zhi-Heng], Berg, A.C.[Alexander C.], Fei-Fei, L.[Li],
Detecting Avocados to Zucchinis: What Have We Done, and Where Are We Going?,
ICCV13(2064-2071)
IEEE DOI 1403
categorical object detection. BibRef

Ehlers, A.[Arne], Scheuermann, B.[Björn], Baumann, F.[Florian], Rosenhahn, B.[Bodo],
Cleaning Up Multiple Detections Caused by Sliding Window Based Object Detectors,
CIARP13(I:456-463).
Springer DOI 1311
BibRef

Tan, T.N.[Tie-Niu], Huang, Y.Z.[Yong-Zhen], Zhang, J.G.[Jun-Ge],
Recent Progress on Object Classification and Detection,
CIARP13(II:1-8).
Springer DOI 1311
BibRef

Nalpantidis, L.[Lazaros], Großmann, B.[Bjarne], Krüger, V.[Volker],
Fast and Accurate Unknown Object Segmentation for Robotic Systems,
ISVC13(II:318-327).
Springer DOI 1311
BibRef

Ren, X.F.[Xiao-Feng], Ramanan, D.[Deva],
Histograms of Sparse Codes for Object Detection,
CVPR13(3246-3253)
IEEE DOI 1309
Feature Learning; Object Detection; Sparse Coding; Supervised Training multiple features, beyond HoGradients. BibRef

Guo, X.[Xin], Liu, D.[Dong], Jou, B.[Brendan], Zhu, M.J.[Mo-Jun], Cai, A.N.[An-Ni], Chang, S.F.[Shih-Fu],
Robust Object Co-detection,
CVPR13(3206-3213)
IEEE DOI 1309
Objects of same category from a pool of similar objects. BibRef

Scharfenberger, C.[Christian], Waslander, S.L.[Steven L.], Zelek, J.S.[John S.], Clausi, D.A.[David A.],
Existence Detection of Objects in Images for Robot Vision Using Saliency Histogram Features,
CRV13(75-82)
IEEE DOI 1308
Feature extraction BibRef

Bria, A.[Alessandro], Marrocco, C.[Claudio], Molinara, M.[Mario], Tortorella, F.[Francesco],
A ranking-based cascade approach for unbalanced data,
ICPR12(3439-3442).
WWW Link. 1302
Use ranking rather than simply error. BibRef

Martelli, S.[Samuele], Cristani, M.[Marco], Bazzani, L.[Loris], Tosato, D.[Diego], Murino, V.[Vittorio],
Joining feature-based and similarity-based pattern description paradigms for object detection,
ICPR12(2702-2705).
WWW Link. 1302
BibRef

Dai, J.F.[Ji-Feng], Feng, J.J.[Jian-Jiang], Zhou, J.[Jie],
Mining sub-categories for object detection,
ICPR12(3260-3263).
WWW Link. 1302
BibRef

Zhang, J.G.[Jun-Ge], Zhao, X.[Xin], Huang, Y.Z.[Yong-Zhen], Huang, K.Q.[Kai-Qi], Tan, T.N.[Tie-Niu],
Semantic windows mining in sliding window based object detection,
ICPR12(3264-3267).
WWW Link. 1302
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Kusuma, G.P.[Gede Putra], Szabo, A.[Attila], Li, Y.Q.[Yi-Qun], Lee, J.A.[Jimmy Addison],
Appearance-based object recognition using weighted longest increasing subsequence,
ICPR12(3668-3671).
WWW Link. 1302
BibRef

Hartl, A.[Andreas], Reitmayr, G.[Gerhard],
Rectangular target extraction for mobile augmented reality applications,
ICPR12(81-84).
WWW Link. 1302
BibRef

Singh, S.[Saurabh], Gupta, A.[Abhinav], Efros, A.A.[Alexei A.],
Unsupervised Discovery of Mid-Level Discriminative Patches,
ECCV12(II: 73-86).
Springer DOI 1210
patches, like parts of objects. BibRef

Russakovsky, O.[Olga], Lin, Y.Q.[Yuan-Qing], Yu, K.[Kai], Fei-Fei, L.[Li],
Object-Centric Spatial Pooling for Image Classification,
ECCV12(II: 1-15).
Springer DOI 1210
Object centered spatial. Infer location, use that to get properties of object and background BibRef

Russakovsky, O.[Olga], Ng, A.Y.[Andrew Y.],
A Steiner tree approach to efficient object detection,
CVPR10(1070-1077).
IEEE DOI 1006
BibRef

Dubout, C.[Charles], Fleuret, F.[François],
Accelerated Training of Linear Object Detectors,
SPTLI13(572-577)
IEEE DOI 1309
BibRef
Earlier:
Exact Acceleration of Linear Object Detectors,
ECCV12(III: 301-311).
Springer DOI 1210
BibRef

Hoiem, D.[Derek], Chodpathumwan, Y.[Yodsawalai], Dai, Q.[Qieyun],
Diagnosing Error in Object Detectors,
ECCV12(III: 340-353).
Springer DOI 1210
BibRef

Doulamis, N.D.[Nikolaos D.], Doulamis, A.D.[Anastasios D.],
Fast and Adaptive Deep Fusion Learning for Detecting Visual Objects,
Concept12(III: 345-354).
Springer DOI 1210
BibRef

Cao, L.[Lu], Kobayashi, Y.[Yoshinori], Kuno, Y.[Yoshinori],
A Spatial-based Approach for Groups of Objects,
ISVC12(II: 597-608).
Springer DOI 1209
locating several identical objects grouped together. BibRef

Nasse, F.[Fabian], Fink, G.A.[Gernot A.],
A Bottom-up Approach for Learning Visual Object Detection Models from Unreliable Sources,
DAGM12(488-497).
Springer DOI 1209
BibRef

Verschae, R.[Rodrigo], Ruiz-del-Solar, J.[Javier],
TCAS: A Multiclass Object Detector for Robot and Computer Vision Applications,
ISVC12(I: 632-641).
Springer DOI 1209
BibRef

Prest, A.[Alessandro], Leistner, C.[Christian], Civera, J.[Javier], Schmid, C.[Cordelia], Ferrari, V.[Vittorio],
Learning object class detectors from weakly annotated video,
CVPR12(3282-3289).
IEEE DOI 1208
BibRef

Liu, K.[Kun], Wang, Q.[Qing], Driever, W.[Wolfgang], Ronneberger, O.[Olaf],
2D/3D rotation-invariant detection using equivariant filters and kernel weighted mapping,
CVPR12(917-924).
IEEE DOI 1208
BibRef

Wang, X.Y.[Xiao-Yu], Hua, G.[Gang], Han, T.X.[Tony X.],
Detection by detections: Non-parametric detector adaptation for a video,
CVPR12(350-357).
IEEE DOI 1208
Trained object detector. BibRef

Neugebauer, C., Cameron-Jones, M., Horton, M.,
Learnt combination in object detector ensembles,
IVCNZ10(1-8).
IEEE DOI 1203
BibRef

Quast, K.[Katharina], Seeger, C.[Christoph], Trivedi, M.M.[Mohan M.], Kaup, A.[Andre],
Boosting based object detection using a geometric model,
ICIP11(3569-3572).
IEEE DOI 1201
BibRef

Zhao, X.Y.[Xin-Yue], Satoh, Y., Takauji, H., Kaneko, S., Iwata, K., Ozaki, R.,
Robust adapted object detection under complex environment,
AVSBS11(261-266).
IEEE DOI 1111
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Porikli, F.M., Ozkan, H.,
Data driven frequency mapping for computationally scalable object detection,
AVSBS11(30-35).
IEEE DOI 1111
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Smirnov, P.[Pavel], Semenov, P.[Piotr], Redkin, A.[Alexander], Chun, A.[Anthony],
Toward Accurate Feature Detectors Performance Evaluation,
CVS11(51-60).
Springer DOI 1109
BibRef

Zhang, G.X.[Gao-Xiang], Jiang, F.[Feng], Zhao, D.B.[De-Bin], Sun, X.S.[Xiao-Shuai], Liu, S.H.[Shao-Hui],
Saliency Detection: A Self-Adaption Sparse Representation Approach,
ICIG11(461-465).
IEEE DOI 1109
BibRef

Chen, G.[Guang], Han, T.X.[Tony X.], Lao, S.H.[Shi-Hong],
Adapting an object detector by considering the worst case: A conservative approach,
CVPR11(1369-1376).
IEEE DOI 1106
BibRef

Kim, H.C.[Hyun-Cheol], Kim, W.Y.[Whoi-Yul],
Salient Region Detection Using Discriminative Feature Selection,
ACIVS11(305-315).
Springer DOI 1108
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Zhang, Z.M.[Zi-Ming], Huang, J.W.[Jia-Wei], Li, Z.N.[Ze-Nian],
Learning Sparse Features On-Line for Image Classification,
ICIAR11(I: 122-131).
Springer DOI 1106
BibRef

Chiusano, G.[Gabriele], Staglianò, A.[Alessandra], Basso, C.[Curzio], Verri, A.[Alessandro],
DCE-MRI Analysis Using Sparse Adaptive Representations,
MLMI11(67-74).
Springer DOI 1109
BibRef

Staglianò, A.[Alessandra], Chiusano, G.[Gabriele], Basso, C.[Curzio], Santoro, M.[Matteo],
Learning Adaptive and Sparse Representations of Medical Images,
MCV10(130-140).
Springer DOI 1009
Sparse coding by learning dictionaries of features. BibRef

Semenovich, D.[Dimitri], Sowmya, A.[Arcot],
Geometry Aware Local Kernels for Object Recognition,
ACCV10(I: 490-503).
Springer DOI 1011
BibRef

Li, H.Y.[Hong-Yu], Chen, L.[Lei],
Removal of false positive in object detection with contour-based classifiers,
ICIP10(3941-3944).
IEEE DOI 1009
after Haar-based detection. BibRef

Schindler, A.[Andreas], Maier, G.[Georg],
Object detection in gray scale images based on invariant polynomial features,
ICIP10(4633-4636).
IEEE DOI 1009
BibRef

Petit, F.[Frederic], Capelle-Laize, A.S.[Anne-Sophie], Carre, P.[Philippe],
Hue-based quaternionic criterion for focused-color extraction,
ICIP10(1617-1620).
IEEE DOI 1009
Extract specific colored region. BibRef

Gao, K.[Ke], Zhang, Y.D.[Yong-Dong], Zhang, W.[Wei], Lin, S.X.[Shou-Xun],
Affine Stable Characteristic based sample expansion for object detection,
CIVR10(422-429).
DOI Link 1007
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Kobayashi, J.[Junya], Yamada, K.[Keiichi],
Detection of Abnormal Objects in a Scene Based on Local Features,
MVA09(34-).
PDF File. 0905
Trained with usual scenes, find things not in the training. BibRef

Su, J.Y.[Jing-Yong], Zhu, Z.Q.[Zhi-Qiang], Srivastava, A.[Anuj], Huffer, F.[Fred],
Detection of Shapes in 2D Point Clouds Generated from Images,
ICPR10(2640-2643).
IEEE DOI 1008
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Cho, M.[Minsu], Shin, Y.M.[Young Min], Lee, K.M.[Kyoung Mu],
Unsupervised detection and segmentation of identical objects,
CVPR10(1617-1624).
IEEE DOI Video of talk:
WWW Link. 1006
Grow from local feature matches. BibRef

Pham, M.T.[Minh-Tri], Gao, Y.[Yang], Hoang, V.D.D.[Viet-Dung D.], Cham, T.J.[Tat-Jen],
Fast polygonal integration and its application in extending Haar-like features to improve object detection,
CVPR10(942-949).
IEEE DOI 1006
Fast technique for arbitrary polygon, not just rectangular window. BibRef

Lehmann, A.[Alain], Leibe, B.[Bastian], Van Gool, L.J.[Luc J.],
Feature-centric Efficient Subwindow Search,
ICCV09(940-947).
IEEE DOI 0909
Searching in object detection.
See also Efficient Subwindow Search: A Branch and Bound Framework for Object Localization. BibRef

Nie, Q.[Qing], Li, W.M.[Wei-Ming], Zhan, S.Y.[Shou-Yi],
Classification Based on SPACT and Visual Saliency,
CISP09(1-5).
IEEE DOI 0910
Modified spatial PACT as local feature descriptor. BibRef

Gao, J.M.[Jing-Min], Sun, Y.[Yan],
The Jag-Wave Feature Detection in 2D Images,
CISP09(1-5).
IEEE DOI 0910
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Wang, A.L.[Ai-Li], Liu, P.G.[Pi-Gang], Chen, Y.S.[Yu-Shi],
Multiwavelet-Based Region of Interest Image Coding,
CISP09(1-4).
IEEE DOI 0910
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Vacura, M.[Miroslav], Svatek, V.[Vojtech], Saathoff, C.[Carsten], Franz, T.[Thomas], Troncy, R.[Raphael],
Describing low-level image features using the COMM ontology,
ICIP08(49-52).
IEEE DOI 0810
Extract low level features with COMM rather than MPEG-7 standard. BibRef

Li, Z.D.[Zhi-Dong], Chen, J.[Jing],
On Semantic Object Detection with Salient Feature,
ISVC08(II: 782-791).
Springer DOI 0812
BibRef

Emaminejad, A., Brookes, M.,
FEUDOR: Feature Extraction Using Distinctive Octagonal Regions,
BMVC08(xx-yy).
PDF File. 0809
BibRef

Mahmood, A.[Arif],
Structure-less object detection using AdaBoost algorithm,
ICMV07(85-90).
IEEE DOI 0712
BibRef

Chin, B.[Barret], Zhang, M.J.[Meng-Jie],
Object Detection Using Neural Networks and Genetic Programming,
EvoIASP08(xx-yy).
Springer DOI 0804
BibRef

Baró, X.[Xavier], Vitrià, J.[Jordi],
Weighted Dissociated Dipoles: An Extended Visual Feature Set,
CVS08(xx-yy).
Springer DOI 0805
representation based on Haar-like filters for use in classification. BibRef

Baró, X.[Xavier], Vitrià, J.[Jordi],
Evolutionary Object Detection by Means of Naïve Bayes Models Estimation,
EvoIASP08(xx-yy).
Springer DOI 0804
BibRef

Wang, W.X.[Wei-Xing],
Size and Shape Measure of Particles by Image Analysis,
IWCIA06(253-262).
Springer DOI 0606
BibRef

Lichtenauer, J.F.[Jeroen F.], Hendriks, E.A.[Emile A.], Reinders, M.J.T.[Marcel J.T.],
Isophote Properties as Features for Object Detection,
CVPR05(II: 649-654).
IEEE DOI 0507
Filters for object detection. BibRef

Mamlouk, A.M.[Amir Madany], Kim, J.T.[Jan T.], Barth, E.[Erhardt], Brauckmann, M.[Michael], Martinetz, T.[Thomas],
One-Class Classification with Subgaussians,
DAGM03(346-353).
Springer DOI 0310
Assume a gaussian distribution, then it is a template match. BibRef

Cucurachi, G.[Giorgio], Tascini, G.[Guido], Piazza, F.[Francesco],
Neural network for region detection,
CIAP97(II: 228-237).
Springer DOI 9709
BibRef

Cho, D.U.[Dong-Uk], Bae, J.J.,
Fuzzy-set based feature extraction for objects of various shapes and appearances,
ICIP96(II: 983-986).
IEEE DOI 9610
BibRef

Davies, E.R., Barker, S.P.,
An analysis of hole detection schemes,
BMVC90(xx-yy).
PDF File. 9009
BibRef

Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Detection Transformer, DETR Applications .