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BibRef
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1506
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0908
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IP(19), No. 12, December 2010, pp. 3232-3242.
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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],
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CirSysVideo(22), No. 5, May 2012, pp. 683-692.
IEEE DOI
1202
BibRef
Earlier: A1, A3, A2:
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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
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Lampert, C.H.[Christoph H.],
Blaschko, M.B.[Matthew B.],
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IJCV(88), No. 2, June 2010, pp. xx-yy.
Springer DOI
1003
BibRef
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ECCV12(V: 242-255).
Springer DOI
1210
BibRef
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Performance evaluation; Object detection; Object matching; Shape
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Weber Local Descriptor (human perception depends not only on the
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WLD: differential excitation and orientation.
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Hyperspectral imagery; Target detection; Spectral matched filter;
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1101
Feature detection; Shift invariance; Multi-scale processing;
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Rahmoune, A.,
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Curvilinear structures; Wiry objects; Descriptor; Detector;
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Definition of a Model-Based Detector of Curvilinear Regions,
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0708
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Murray, P.,
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IP(20), No. 7, July 2011, pp. 1938-1948.
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IEEE DOI
1201
BibRef
Earlier:
CVPR10(3539-3546).
IEEE DOI
1006
BibRef
Vempati, S.[Sreekanth],
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IEEE DOI
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BibRef
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0909
See also Learning The Discriminative Power-Invariance Trade-Off.
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1110
Award, BMVC, HM Poster.
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1202
Collective-reward; Object detection; Semi-transparency; Transparency; Glass.
Both transmission and reflection.
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IEEE DOI
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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
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Image matching using peak signal-to-noise ratio-based occlusion
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IET-IPR(6), No. 5, 2012, pp. 483-495.
DOI Link
1210
locate objects with partial occlusions.
Compare to correlation based methods.
BibRef
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Tirkkonen, O.,
A Blind Time-Reversal Detector in the Presence of Channel Correlation,
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1304
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SimLocator: robust locator of similar objects in images,
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DOI Link
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Survey, Object Class. Object class detection, also known as category-level object detection,
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1402
BibRef
Earlier:
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ECCV12(III: 539-552).
Springer DOI
1210
Sampling rather than all points.
BibRef
Niitsu, Y.S.[Yasu-Shi],
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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
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1403
Image segmentation
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BibRef
Earlier:
Exploiting Features:
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ACCV12(I:446-459).
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1304
Computational modeling
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Real-time texture-less object detection
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Chong, N.S.[Nguan Soon],
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The Role of Diffusion in Figure Hunt Games,
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Finding waldo.
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High-Order Statistics of Weber Local Descriptors for Image
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Adaptation models
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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],
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Adjusted Spectral Matched Filter for Target Detection in
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Pedersoli, M.[Marco],
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PR(48), No. 5, 2015, pp. 1844-1853.
Elsevier DOI
1502
BibRef
Earlier: A1, A2, A3, Only:
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IEEE DOI
1106
Object recognition
BibRef
Gonfaus, J.M.[Josep M.],
Pedersoli, M.[Marco],
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1506
Object recognition
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Villanueva, J.J.[Juan J.],
Recursive Coarse-to-Fine Localization for Fast Object Detection,
ECCV10(VI: 280-293).
Springer DOI
1009
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Santosh, K.C.,
Wendling, L.[Laurent],
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Thoma, G.R.[George R.],
Overlaid Arrow Detection for Labeling Regions of Interest in
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IEEE_Int_Sys(31), No. 3, May 2016, pp. 66-75.
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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
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PRL(86), No. 1, 2017, pp. 56-61.
Elsevier DOI
1702
Object detection
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Salient object detection with low-rank approximation and l2,1-norm
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IVC(57), No. 1, 2017, pp. 67-77.
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1702
Background: low rank; objects: sparse.
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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
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Prakash, T.[Tanmay],
Kak, A.C.[Avinash C.],
Active learning for designing detectors for infrequently occurring
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CVIU(170), 2018, pp. 92-108.
Elsevier DOI
1806
Object detection, Satellite imagery, Active learning,
Distributed computing, Feature selection, Pattern recognition
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Wang, R.[Rui],
Xu, J.W.[Jing-Wen],
Han, T.X.[Tony X.],
Object instance detection with pruned Alexnet and extended training
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SP:IC(70), 2019, pp. 145-156.
Elsevier DOI
1812
Object instance detection, Pruned Alexnet,
Binarized normed gradient, Data extension
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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
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RS(10), No. 12, 2018, pp. xx-yy.
DOI Link
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Li, Y.[Yan],
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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
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Wu, X.[Xing],
Zhang, X.[Xia],
Wang, N.[Nan],
Cen, Y.[Yi],
Joint Sparse and Low-Rank Multi-Task Learning with Extended
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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
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IP(29), 2020, pp. 2947-2962.
IEEE DOI
2002
Survey, Objetc Detection. Object detection, Cameras, Sensors,
Laser radar, Task analysis,
deep learning
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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
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RealTimeIP(17), No. 3, June 2020, pp. 655-665.
Springer DOI
2006
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Zhang, R.,
Huang, Y.,
Pu, M.,
Zhang, J.,
Guan, Q.,
Zou, Q.,
Ling, H.,
Object Discovery From a Single Unlabeled Image by Mining Frequent
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IEEE DOI
2009
Feature extraction, Annotations, Saliency detection, Training,
Data mining, Task analysis, Semantics, Object discovery,
convolutional neural networks
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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
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ECCV20(IX:733-748).
Springer DOI
2011
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Yan, J.Q.[Jiang-Qiao],
Zhao, L.J.[Liang-Jin],
Diao, W.H.[Wen-Hui],
Wang, H.Q.[Hong-Qi],
Sun, X.[Xian],
AF-EMS Detector: Improve the Multi-Scale Detection Performance of the
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RS(13), No. 2, 2021, pp. xx-yy.
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García-Domínguez, M.[Manuel],
Domínguez, C.[César],
Heras, J.[Jónathan],
Mata, E.[Eloy],
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PRL(145), 2021, pp. 135-140.
Elsevier DOI
2104
AutoML, Deep learning, Object detection, Transfer learning
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Chen, J.[Jin],
Wu, X.X.[Xin-Xiao],
Duan, L.X.[Li-Xin],
Chen, L.[Lin],
Sequential Instance Refinement for Cross-Domain Object Detection in
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IP(30), 2021, pp. 3970-3984.
IEEE DOI
2104
Object detection, Feature extraction, Detectors,
Reinforcement learning, Proposals, Task analysis.
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Wang, H.S.[Hong-Song],
Liao, S.C.[Sheng-Cai],
Shao, L.[Ling],
AFAN: Augmented Feature Alignment Network for Cross-Domain Object
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IP(30), 2021, pp. 4046-4056.
IEEE DOI
2104
Training, Object detection, Feature extraction, Detectors,
Generators, Semantics, Proposals, Object detection,
unsupervised domain adaptation
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Guo, Y.G.[Ya-Guang],
Zou, Q.[Qi],
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A coarse to fine network for fast and accurate object detection in
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IET-CV(15), No. 4, 2021, pp. 274-282.
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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, Computer architecture,
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
Zhang, M.H.[Ming-Hua],
Xu, S.[Shubo],
Song, W.[Wei],
He, Q.[Qi],
Wei, Q.[Quanmiao],
Lightweight Underwater Object Detection Based on YOLO v4 and
Multi-Scale Attentional Feature Fusion,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link
2112
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, G.B.[Guan-Bo],
Ding, H.W.[Hong-Wei],
Li, B.[Bo],
Nie, R.C.[Ren-Can],
Zhao, Y.F.[Yi-Fan],
Trident-YOLO: Improving the precision and speed of mobile device
object detection,
IET-IPR(16), No. 1, 2022, pp. 145-157.
DOI Link
2112
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
Zhang, C.[Cheng],
Pan, T.Y.[Tai-Yu],
Li, Y.D.[Yan-Dong],
Hu, H.X.[He-Xiang],
Xuan, D.[Dong],
Changpinyo, S.[Soravit],
Gong, B.Q.[Bo-Qing],
Chao, W.L.[Wei-Lun],
MosaicOS: A Simple and Effective Use of Object-Centric Images for
Long-Tailed Object Detection,
ICCV21(407-417)
IEEE DOI
2203
Training, Image segmentation, Computational modeling,
Object detection, Detectors, Recognition and classification,
Transfer/Low-shot/Semi/Unsupervised Learning
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
Zhang, T.[Tao],
Jin, B.[Bo],
Jia, W.J.[Wen-Jing],
An anchor-free object detector based on soften optimized
bi-directional FPN,
CVIU(218), 2022, pp. 103410.
Elsevier DOI
2205
Object detection, Anchor-free, Feature Pyramid Network, Soft-weighted
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
Xing, Z.Q.[Zhi-Qiang],
Chen, X.[Xi],
Pang, F.Q.[Feng-Qian],
DD-YOLO: An object detection method combining knowledge distillation
and Differentiable Architecture Search,
IET-CV(16), No. 5, 2022, pp. 418-430.
DOI Link
2207
DARTS, high-efficiency detection, knowledge distillation, YOLOv4
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
Liu, J.R.[Jing-Ren],
Chen, Y.[Yi],
Liu, H.J.[Hua-Jun],
Zhang, H.F.[Hao-Feng],
Zhang, Y.D.[Yu-Dong],
From Less to More: Progressive Generalized Zero-Shot Detection With
Curriculum Learning,
ITS(23), No. 10, October 2022, pp. 19016-19029.
IEEE DOI
2210
Task analysis, Visualization, Generators, Training, Object detection,
Semantics, Proposals, Object detection, generative adversarial network (GAN)
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
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
Wang, L.[Lili],
Ni, Q.[Qinghang],
Chen, C.[Chen],
Yang, H.[Hailu],
Lightweight target detection algorithm based on improved YOLOv4,
IET-IPR(16), No. 14, 2022, pp. 3805-3813.
DOI Link
2212
BibRef
Liu, C.[Chen],
Yang, D.[Degang],
Tang, L.[Liu],
Zhou, X.[Xun],
Deng, Y.[Yi],
A Lightweight Object Detector Based on Spatial-Coordinate
Self-Attention for UAV Aerial Images,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link
2301
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
S, A.K.[Arun Kumar],
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
BibRef
Zand, M.[Mohsen],
Etemad, A.[Ali],
Greenspan, M.[Michael],
ObjectBox: From Centers to Boxes for Anchor-Free Object Detection,
ECCV22(X:390-406).
Springer DOI
2211
BibRef
Maaz, M.[Muhammad],
Rasheed, H.[Hanoona],
Khan, S.[Salman],
Khan, F.S.[Fahad Shahbaz],
Anwer, R.M.[Rao Muhammad],
Yang, M.H.[Ming-Hsuan],
Class-Agnostic Object Detection with Multi-modal Transformer,
ECCV22(X:512-531).
Springer DOI
2211
BibRef
Hess, G.[Georg],
Petersson, C.[Christoffer],
Svensson, L.[Lennart],
Object Detection as Probabilistic Set Prediction,
ECCV22(X:550-566).
Springer DOI
2211
BibRef
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
BibRef
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,
Pattern recognition, 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, Computer architecture,
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, Computer architecture,
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
Wu, A.[Aming],
Deng, C.[Cheng],
Single-Domain Generalized Object Detection in Urban Scene via
Cyclic-Disentangled Self-Distillation,
CVPR22(837-846)
IEEE DOI
2210
Code, Object Detection.
WWW Link. Training, Representation learning, Visualization, Annotations,
Object detection, Detectors, Performance gain,
Transfer/low-shot/long-tail learning
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, Computer architecture,
Market research, Pattern recognition
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
Sun, Z.Q.[Zhi-Qing],
Cao, S.[Shengcao],
Yang, Y.M.[Yi-Ming],
Kitani, K.[Kris],
Rethinking Transformer-based Set Prediction for Object Detection,
ICCV21(3591-3600)
IEEE DOI
2203
Training, Codes, Computational modeling, Object detection,
Transformers, Optimization,
BibRef
Liu, F.F.[Fan-Fan],
Wei, H.R.[Hao-Ran],
Zhao, W.Z.[Wen-Zhe],
Li, G.Z.[Guo-Zhen],
Peng, J.Q.[Jing-Quan],
Li, Z.[Zihao],
WB-DETR: Transformer-Based Detector without Backbone,
ICCV21(2959-2967)
IEEE DOI
2203
Pipelines, Detectors, Object detection, Transformers,
Feature extraction, Decoding,
BibRef
Dai, X.Y.[Xi-Yang],
Chen, Y.P.[Yin-Peng],
Yang, J.W.[Jian-Wei],
Zhang, P.C.[Peng-Chuan],
Yuan, L.[Lu],
Zhang, L.[Lei],
Dynamic DETR: End-to-End Object Detection with Dynamic Attention,
ICCV21(2968-2977)
IEEE DOI
2203
Training, Visualization, Object detection, Color, Transformers,
Feature extraction, Encoding,
Vision applications and systems
BibRef
Yu, F.[Fuxun],
Wang, D.[Di],
Chen, Y.P.[Yin-Peng],
Karianakis, N.[Nikolaos],
Shen, T.[Tong],
Yu, P.[Pei],
Lymberopoulos, D.[Dimitrios],
Lu, S.[Sidi],
Shi, W.S.[Wei-Song],
Chen, X.[Xiang],
SC-UDA: Style and Content Gaps aware Unsupervised Domain Adaptation
for Object Detection,
WACV22(1061-1070)
IEEE DOI
2202
Costs, Training data, Object detection, Detectors,
Benchmark testing, Feature extraction, Transfer, Few-shot,
Semi- and Un- supervised Learning Scene Understanding
BibRef
Hnewa, M.[Mazin],
Radha, H.[Hayder],
Multiscale Domain Adaptive Yolo for Cross-Domain Object Detection,
ICIP21(3323-3327)
IEEE DOI
2201
Training, Instruments, Object detection, Detectors,
Feature extraction, Real-time systems, Object detection, Domain shift
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
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, Pattern recognition
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, Pattern recognition
BibRef
Deng, J.[Jinhong],
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
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.[Xinghao],
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, Pattern recognition, 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, Pattern recognition
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
Gao, P.[Peng],
Zheng, M.H.[Ming-Hang],
Wang, X.G.[Xiao-Gang],
Dai, J.F.[Ji-Feng],
Li, H.S.[Hong-Sheng],
Fast Convergence of DETR with Spatially Modulated Co-Attention,
ICCV21(3601-3610)
IEEE DOI
2203
Training, Representation learning, Visualization, Schedules, Costs,
Object detection, Transformers, Vision applications and systems
BibRef
Dai, Z.G.[Zhi-Gang],
Cai, B.[Bolun],
Lin, Y.G.[Yu-Geng],
Chen, J.Y.[Jun-Ying],
UP-DETR: Unsupervised Pre-training for Object Detection with
Transformers,
CVPR21(1601-1610)
IEEE DOI
2111
Location awareness, Training data, Object detection, Transformers,
Feature extraction, Natural language processing, Pattern recognition
BibRef
Ma, Y.C.[Yu-Chen],
Liu, S.T.[Song-Tao],
Li, Z.[Zeming],
Sun, J.[Jian],
IQDet:
Instance-wise Quality Distribution Sampling for Object Detection,
CVPR21(1717-1725)
IEEE DOI
2111
Training, Visualization, Semantics, Detectors, Object detection,
Mixture models, Feature extraction
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, Pattern recognition, 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.[Qianli],
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
BibRef
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
BibRef
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
BibRef
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
BibRef
Li, J.D.[Jun-De],
Ghosh, S.[Swaroop],
Quantum-soft Qubo Suppression for Accurate Object Detection,
ECCV20(XXIX: 158-173).
Springer DOI
2010
BibRef
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
Computer architecture, 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,
Computer architecture, 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
Zhu, P.,
Wang, H.,
Saligrama, V.,
Don't Even Look Once: Synthesizing Features for Zero-Shot Detection,
CVPR20(11690-11699)
IEEE DOI
2008
Detectors, Visualization, Feature extraction, Training, Semantics,
Object detection, Measurement
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
Ren, Z.,
Yu, Z.,
Yang, X.,
Liu, M.,
Lee, Y.J.,
Schwing, A.G.,
Kautz, J.,
Instance-Aware, Context-Focused, and Memory-Efficient Weakly
Supervised Object Detection,
CVPR20(10595-10604)
IEEE DOI
2008
Proposals, Object detection, Training, Memory management, Detectors,
Task analysis, Face
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
Li, J.C.[Jia-Chen],
Cheng, B.[Bowen],
Feris, R.[Rogerio],
Xiong, J.J.[Jin-Jun],
Huang, T.S.[Thomas S.],
Hwu, W.M.[Wen-Mei],
Shi, H.[Humphrey],
Pseudo-IoU: Improving Label Assignment in Anchor-Free Object
Detection,
MAI21(2378-2387)
IEEE DOI
2109
Measurement, Training, Location awareness,
Computational modeling, Object detection
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
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.,
Li, Z.,
Zhang, T.,
Peng, C.,
Yu, G.,
Zhang, X.,
Li, J.,
Sun, J.,
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
Wu, Z.,
Suresh, K.,
Narayanan, P.,
Xu, H.,
Kwon, H.,
Wang, Z.,
Delving Into Robust Object Detection From Unmanned Aerial Vehicles:
A Deep Nuisance Disentanglement Approach,
ICCV19(1201-1210)
IEEE DOI
2004
Code, Object Detection.
WWW Link. autonomous aerial vehicles, learning (artificial intelligence),
object detection, transforms, free meta-data, UAV images, Detectors
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
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,
Electronics packaging, Streaming media, Cats
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
Redmon, J.[Joseph],
Farhadi, A.[Ali],
YOLO9000: Better, Faster, Stronger,
CVPR17(6517-6525)
IEEE DOI
1711
Award, CVPR, HM. Detectors, Feature extraction, Image resolution, Object detection,
Real-time systems, Training
Real time, 9000 object categories.
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
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Object Detection in Low-Resolution Image via Sparse Representation,
MMMod15(I: 234-245).
Springer DOI
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reconstruct higher resolution image for detection.
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ICPR14(2329-2334)
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Databases
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Compound Exemplar Based Object Detection by Incremental Random Forest,
ICPR14(2407-2412)
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Dynamic programming
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Riabchenko, E.[Ekaterina],
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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
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ACCV16(IV: 256-272).
Springer DOI
1704
BibRef
Peng, X.C.[Xing-Chao],
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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
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Deep CORAL: Correlation Alignment for Deep Domain Adaptation,
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1611
BibRef
Earlier:
Subspace Distribution Alignment for Unsupervised Domain Adaptation,
BMVC15(xx-yy).
DOI Link
1601
BibRef
Earlier:
From Virtual to Reality:
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BMVC14(xx-yy).
HTML Version.
1410
BibRef
Russakovsky, O.[Olga],
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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],
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Baumann, F.[Florian],
Rosenhahn, B.[Bodo],
Cleaning Up Multiple Detections Caused by Sliding Window Based Object
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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],
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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.
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Guo, X.[Xin],
Liu, D.[Dong],
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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.
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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
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Li, Y.[Yali],
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
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Bria, A.[Alessandro],
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ICPR12(3439-3442).
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WWW Link.
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Zhang, J.G.[Jun-Ge],
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Rectangular target extraction for mobile augmented reality applications,
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1302
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Efros, A.A.[Alexei A.],
Unsupervised Discovery of Mid-Level Discriminative Patches,
ECCV12(II: 73-86).
Springer DOI
1210
patches, like parts of objects.
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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
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Russakovsky, O.[Olga],
Ng, A.Y.[Andrew Y.],
A Steiner tree approach to efficient object detection,
CVPR10(1070-1077).
IEEE DOI
1006
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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
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Cao, L.[Lu],
Kobayashi, Y.[Yoshinori],
Kuno, Y.[Yoshinori],
A Spatial-based Approach for Groups of Objects,
ISVC12(II: 597-608).
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1209
locating several identical objects grouped together.
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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,
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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,
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IEEE DOI
1208
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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.
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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
BibRef
Porikli, F.M.,
Ozkan, H.,
Data driven frequency mapping for computationally scalable object
detection,
AVSBS11(30-35).
IEEE DOI
1111
BibRef
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
BibRef
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.
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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.
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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
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CIVR10(422-429).
DOI Link
1007
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Kobayashi, J.[Junya],
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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.
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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],
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Lee, K.M.[Kyoung Mu],
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CVPR10(1617-1624).
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1006
Grow from local feature matches.
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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.
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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.
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Gao, J.M.[Jing-Min],
Sun, Y.[Yan],
The Jag-Wave Feature Detection in 2D Images,
CISP09(1-5).
IEEE DOI
0910
BibRef
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
BibRef
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.
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Li, Z.D.[Zhi-Dong],
Chen, J.[Jing],
On Semantic Object Detection with Salient Feature,
ISVC08(II: 782-791).
Springer DOI
0812
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Emaminejad, A.,
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FEUDOR: Feature Extraction Using Distinctive Octagonal Regions,
BMVC08(xx-yy).
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0809
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Mahmood, A.[Arif],
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Chin, B.[Barret],
Zhang, M.J.[Meng-Jie],
Object Detection Using Neural Networks and Genetic Programming,
EvoIASP08(xx-yy).
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0804
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Baró, X.[Xavier],
Vitrià, J.[Jordi],
Weighted Dissociated Dipoles: An Extended Visual Feature Set,
CVS08(xx-yy).
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0805
representation based on Haar-like filters for use in classification.
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Baró, X.[Xavier],
Vitrià, J.[Jordi],
Evolutionary Object Detection by Means of Naïve Bayes
Models Estimation,
EvoIASP08(xx-yy).
Springer DOI
0804
BibRef
Jia, W.J.[Wen-Jing],
Tien, D.[David],
He, X.J.[Xiang-Jian],
Hope, B.A.[Brian A.],
Wu, Q.A.[Qi-Ang],
Applying Local Cooccurring Patterns for Object Detection from Aerial
Images,
Visual07(478-489).
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0706
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).
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9709
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Cho, D.U.[Dong-Uk],
Bae, J.J.,
Fuzzy-set based feature extraction for objects of various shapes and
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ICIP96(II: 983-986).
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Davies, E.R.,
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An analysis of hole detection schemes,
BMVC90(xx-yy).
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9009
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Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Blob Detection .