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PAMI(39), No. 7, July 2017, pp. 1476-1481.
IEEE DOI 1706
Detectors, Feature extraction, Object detection, Proposals, Support vector machines, Training, CNN, Object detection, convolutional, feature, map BibRef

Wang, L.[Lei], Zhang, B.C.[Bao-Chang], Han, J.G.[Jun-Gong], Shen, L.L.[Lin-Lin], Qian, C.S.[Cheng-Shan],
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SP:IC(47), No. 1, 2016, pp. 490-499.
Elsevier DOI 1610
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Guo, H., Wang, J., Gao, Y., Li, J., Lu, H.,
Multi-View 3D Object Retrieval With Deep Embedding Network,
IP(25), No. 12, December 2016, pp. 5526-5537.
IEEE DOI 1612
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Rajchl, M., Lee, M.C.H.[M. C. H.], Oktay, O., Kamnitsas, K., Passerat-Palmbach, J., Bai, W., Damodaram, M., Rutherford, M.A., Hajnal, J.V., Kainz, B., Rueckert, D.,
DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks,
MedImg(36), No. 2, February 2017, pp. 674-683.
IEEE DOI 1702
Biological neural networks BibRef

Wei, Y., Zhao, Y., Lu, C., Wei, S., Liu, L., Zhu, Z., Yan, S.,
Cross-Modal Retrieval With CNN Visual Features: A New Baseline,
Cyber(47), No. 2, February 2017, pp. 449-460.
IEEE DOI 1702
feature extraction BibRef

Long, Y.[Yang], Gong, Y.P.[Yi-Ping], Xiao, Z.F.[Zhi-Feng], Liu, Q.[Qing],
Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks,
GeoRS(55), No. 5, May 2017, pp. 2486-2498.
IEEE DOI 1705
Fourier transforms, convolution, geophysical image processing, image classification, neural nets, object detection, accurate object localization, dimension reduction model, BibRef

Long, Y.[Yang], Zhai, X.F.[Xiao-Fang], Wan, Q.[Qiao], Tan, X.W.[Xiao-Wei],
Object Localization in Weakly Labeled Remote Sensing Images Based on Deep Convolutional Features,
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Pang, S.M.[Shan-Min], Zhu, J.H.[Ji-Hua], Wang, J.X.[Jia-Xing], Ordonez, V.[Vicente], Xue, J.R.[Jian-Ru],
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PR(83), 2018, pp. 150-160.
Elsevier DOI 1808
Object retrieval, Replicator equation, Deep feature selection, Deep feature weighting BibRef

Yousif, H.[Hayder], Yuan, J.[Jianhe], Kays, R.[Roland], He, Z.H.[Zhi-Hai],
Object detection from dynamic scene using joint background modeling and fast deep learning classification,
JVCIR(55), 2018, pp. 802-815.
Elsevier DOI 1809
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Object segmentation in the deep neural network feature domain from highly cluttered natural scenes,
ICIP17(3095-3099)
IEEE DOI 1803
Human-animal detection, Camera-trap images, Background subtraction, Deep convolutional neural networks, Wildlife monitoring. Animals, Computational modeling, Feature extraction, Image representation, Image segmentation, Proposals, Semantics, object detection BibRef

Chen, T.[Tao], Lu, S.J.[Shi-Jian], Fan, J.Y.[Jia-Yuan],
S-CNN: Subcategory-Aware Convolutional Networks for Object Detection,
PAMI(40), No. 10, October 2018, pp. 2522-2528.
IEEE DOI 1809
Detectors, Training, Object detection, Proposals, Feature extraction, Robustness, Deformable models, Subcategory, object detection, subcategory-aware CNN BibRef

Fu, H.[Huan], Gong, M.M.[Ming-Ming], Wang, C.H.[Chao-Hui], Tao, D.C.[Da-Cheng],
MoE-SPNet: A mixture-of-experts scene parsing network,
PR(84), 2018, pp. 226-236.
Elsevier DOI 1809
Scene parsing, Mixture-of-experts, Attention, Convolutional neural network BibRef

Lim, L.A.[Long Ang], Keles, H.Y.[Hacer Yalim],
Foreground segmentation using convolutional neural networks for multiscale feature encoding,
PRL(112), 2018, pp. 256-262.
Elsevier DOI 1809
Foreground segmentation, Background subtraction, Deep learning, Convolutional neural networks, Video surveillance, Pixel classification BibRef

Wang, Y.[Yida], Deng, W.H.[Wei-Hong],
Generative Model With Coordinate Metric Learning for Object Recognition Based on 3D Models,
IP(27), No. 12, December 2018, pp. 5813-5826.
IEEE DOI 1810
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Self-restraint object recognition by model based CNN learning,
ICIP16(654-658)
IEEE DOI 1610
belief networks, feature extraction, image reconstruction, image representation, image segmentation, metric learning Data models BibRef

Zhu, Y.S.[You-Song], Zhao, C.Y.[Chao-Yang], Guo, H.Y.[Hai-Yun], Wang, J.Q.[Jin-Qiao], Zhao, X.[Xu], Lu, H.Q.[Han-Qing],
Attention CoupleNet: Fully Convolutional Attention Coupling Network for Object Detection,
IP(28), No. 1, January 2019, pp. 113-126.
IEEE DOI 1810
convolution, feature extraction, feedforward neural nets, graph theory, image classification, image representation, local parts BibRef

Deng, Z.P.[Zhi-Peng], Sun, H.[Hao], Zhou, S.L.[Shi-Lin], Zhao, J.P.[Juan-Ping], Lei, L.[Lin], Zou, H.X.[Huan-Xin],
Multi-scale object detection in remote sensing imagery with convolutional neural networks,
PandRS(145), 2018, pp. 3-22.
Elsevier DOI 1810
Object detection, Deep learning, Convolutional neural networks, Multi-modal remote sensing images BibRef

Shuai, B., Ding, H., Liu, T., Wang, G., Jiang, X.,
Toward Achieving Robust Low-Level and High-Level Scene Parsing,
IP(28), No. 3, March 2019, pp. 1378-1390.
IEEE DOI 1812
feedforward neural nets, image representation, image segmentation, object detection, segmentation network, skip layers BibRef

Sun, F.C.[Fu-Chun], Kong, T.[Tao], Huang, W.B.[Wen-Bing], Tan, C.Q.[Chuan-Qi], Fang, B.[Bin], Liu, H.P.[Hua-Ping],
Feature Pyramid Reconfiguration With Consistent Loss for Object Detection,
IP(28), No. 10, October 2019, pp. 5041-5051.
IEEE DOI 1909
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Deep Feature Pyramid Reconfiguration for Object Detection,
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Springer DOI 1810
Object detection, Detectors, Feature extraction, Semantics, Training, Proposals, Entropy, Accurate object detection, consistent loss BibRef

Li, Y.S.[Yan-Sheng], Zhang, Y.J.[Yong-Jun], Huang, X.[Xin], Yuille, A.L.[Alan L.],
Deep Networks Under Scene-Level Supervision for Multi-Class Geospatial Object Detection from Remote Sensing Images,
PandRS(146), 2018, pp. 182-196.
Elsevier DOI 1812
Multi-class geospatial object detection, Deep networks, Scene-level supervision, Class-specific activation weights BibRef

Xiong, S.Z.[Sheng-Zhou], Tan, Y.H.[Yi-Hua], Li, Y.S.[Yan-Sheng], Wen, C.[Cai], Yan, P.[Pei],
Subtask Attention Based Object Detection in Remote Sensing Images,
RS(13), No. 10, 2021, pp. xx-yy.
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Li, Y.S.[Yan-Sheng], Zhang, Y.J.[Yong-Jun], Zhu, Z.H.[Zhi-Hui],
Error-Tolerant Deep Learning for Remote Sensing Image Scene Classification,
Cyber(51), No. 4, April 2021, pp. 1756-1768.
IEEE DOI 2103
Remote sensing, Noise measurement, Machine learning, Robustness, Collaboration, Error correction, RSSC-oriented error-tolerant deep learning (RSSC-ETDL) BibRef

Sangineto, E.[Enver], Nabi, M.[Moin], Culibrk, D.[Dubravko], Sebe, N.[Nicu],
Self Paced Deep Learning for Weakly Supervised Object Detection,
PAMI(41), No. 3, March 2019, pp. 712-725.
IEEE DOI 1902
Training, Protocols, Object detection, Reliability, Task analysis, Machine learning, Detectors, Weakly supervised learning, training protocol BibRef

Soviany, P.[Petru], Ionescu, R.T.[Radu Tudor], Rota, P.[Paolo], Sebe, N.[Nicu],
Curriculum self-paced learning for cross-domain object detection,
CVIU(204), 2021, pp. 103166.
Elsevier DOI 2102
Object detection, Cross-domain, Unsupervised domain adaptation, Curriculum learning, Self-paced learning BibRef

Zhuo, X.Y.[Xiang-Yu], Fraundorfer, F.[Friedrich], Kurz, F.[Franz], Reinartz, P.[Peter],
Automatic Annotation of Airborne Images by Label Propagation Based on a Bayesian-CRF Model,
RS(11), No. 2, 2019, pp. xx-yy.
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Siméoni, O.[Oriane], Iscen, A.[Ahmet], Tolias, G.[Giorgos], Avrithis, Y.[Yannis], Chum, O.[Ondrej],
Graph-based particular object discovery,
MVA(30), No. 2, March 2019, pp. 243-254.
Springer DOI 1904
BibRef
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Unsupervised Object Discovery for Instance Recognition,
WACV18(1745-1754)
IEEE DOI 1806
With background clutter. feedforward neural nets, graph theory, image representation, image retrieval, object detection, BibRef

Shen, Z.Y.[Zong-Ying], Han, S.Y.[Shiang-Yu], Fu, L.C.[Li-Chen], Hsiao, P.Y.[Pei-Yung], Lau, Y.C.[Yo-Chung], Chang, S.J.[Sheng-Jen],
Deep convolution neural network with scene-centric and object-centric information for object detection,
IVC(85), 2019, pp. 14-25.
Elsevier DOI 1905
Deep learning, Convolutional neural networks, Real-time object detection, Scene information BibRef

Xie, W.Y.[Wei-Ying], Qin, H.[Haonan], Li, Y.S.[Yun-Song], Wang, Z.[Zhuo], Lei, J.[Jie],
A Novel Effectively Optimized One-Stage Network for Object Detection in Remote Sensing Imagery,
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Peng, H.Y.[Han-Yu], Chen, S.F.[Shi-Feng],
BDNN: Binary convolution neural networks for fast object detection,
PRL(125), 2019, pp. 91-97.
Elsevier DOI 1909
Deep learning, Object detection, Network compression BibRef

López-Tapia, S., Molina, R., de la Blanca, N.P.,
Deep CNNs for Object Detection Using Passive Millimeter Sensors,
CirSysVideo(29), No. 9, September 2019, pp. 2580-2589.
IEEE DOI 1909
Image segmentation, Feature extraction, Convolution, Image sensors, Sensors, Classification, deep learning, security BibRef

Song, X., Jiang, S., Wang, B., Chen, C., Chen, G.,
Image Representations With Spatial Object-to-Object Relations for RGB-D Scene Recognition,
IP(29), No. 1, 2020, pp. 525-537.
IEEE DOI 1910
image classification, image coding, image representation, object detection, recurrent neural nets, tensors, BibRef

Song, K., Yang, H., Yin, Z.,
Multi-Scale Attention Deep Neural Network for Fast Accurate Object Detection,
CirSysVideo(29), No. 10, October 2019, pp. 2972-2985.
IEEE DOI 1910
data mining, feature extraction, learning (artificial intelligence), neural nets, deep neural network BibRef

Lee, H., Eum, S., Kwon, H.,
ME R-CNN: Multi-Expert R-CNN for Object Detection,
IP(29), No. , 2020, pp. 1030-1044.
IEEE DOI 1911
Training, Object detection, Shape, Optimization, Task analysis, Pipelines, Multiple experts, expert assigner BibRef

Hu, X.G.[Xue-Gang], Yang, H.G.[Hong-Guang],
DRU-net: a novel U-net for biomedical image segmentation,
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Fang, F., Li, L., Zhu, H., Lim, J.,
Combining Faster R-CNN and Model-Driven Clustering for Elongated Object Detection,
IP(29), 2020, pp. 2052-2065.
IEEE DOI 2001
Proposals, Object detection, Adaptation models, Clustering algorithms, Detectors, Sports equipment, Training, likelihood estimation BibRef

Shen, Z.Q.[Zhi-Qiang], Liu, Z.[Zhuang], Li, J.G.[Jian-Guo], Jiang, Y.G.[Yu-Gang], Chen, Y.R.[Yu-Rong], Xue, X.Y.[Xiang-Yang],
Object Detection from Scratch with Deep Supervision,
PAMI(42), No. 2, February 2020, pp. 398-412.
IEEE DOI 2001
BibRef
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DSOD: Learning Deeply Supervised Object Detectors from Scratch,
ICCV17(1937-1945)
IEEE DOI 1802
Object detection, Detectors, Task analysis, Training, Computational modeling, Linear programming, Data models, densely connected layers. image classification, learning (artificial intelligence), DSOD, Training data BibRef

Li, Y., Wang, S.,
HAR-Net: Joint Learning of Hybrid Attention for Single-Stage Object Detection,
IP(29), 2020, pp. 3092-3103.
IEEE DOI 2002
Object detection, deep neural networks, hybrid attention mechanism, single-stage detection, joint learning BibRef

Dong, Z.P.[Zhi-Peng], Wang, M.[Mi], Wang, Y.L.[Yan-Li], Zhu, Y.[Ying], Zhang, Z.Q.[Zhi-Qi],
Object Detection in High Resolution Remote Sensing Imagery Based on Convolutional Neural Networks With Suitable Object Scale Features,
GeoRS(58), No. 3, March 2020, pp. 2104-2114.
IEEE DOI 2003
Convolutional neural network (CNN), deep learning, high-resolution remote sensing image, object detection, object scare BibRef

Dong, Z.P.[Zhi-Peng], Wang, M.[Mi], Wang, Y.L.[Yan-Li], Liu, Y.X.[Yan-Xiong], Feng, Y.K.[Yi-Kai], Xu, W.X.[Wen-Xue],
Multi-Oriented Object Detection in High-Resolution Remote Sensing Imagery Based on Convolutional Neural Networks with Adaptive Object Orientation Features,
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Cherloo, M.N.[Mohammad Norizadeh], Shiri, M.[Milad], Daliri, M.R.[Mohammad Reza],
An enhanced HMAX model in combination with SIFT algorithm for object recognition,
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Springer DOI 2003
Hierarchical model and X (a feedforward network). BibRef

Zhang, Z.W.[Zhe-Wei], Jing, T.[Tao], Tian, C.H.[Chun-Hua], Cui, P.F.[Peng-Fei], Li, X.J.[Xue-Jing], Gao, M.L.[Mei-Lin],
Objects Discovery Based on Co-Occurrence Word Model With Anchor-Box Polishing,
CirSysVideo(30), No. 3, March 2020, pp. 632-645.
IEEE DOI 2003
Training, Visualization, Deep learning, Computational modeling, Feature extraction, Principal component analysis, LDA, region of interest BibRef

Zhu, X.Y.[Xin-Yu], Zhang, J.[Jun], Chen, G.S.[Geng-Sheng],
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Wu, M.H.[Ming-Hu], Yue, H.H.[Han-Hui], Wang, J.[Juan], Huang, Y.X.[Yong-Xi], Liu, M.[Min], Jiang, Y.H.[Yu-Han], Ke, C.[Cong], Zeng, C.[Cheng],
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Gated CNN: Integrating multi-scale feature layers for object detection,
PR(105), 2020, pp. 107131.
Elsevier DOI 2006
Gated CNN, object detection, multi-scale feature layers, explainable CNN BibRef

Zhou, C., Yuan, J.,
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IEEE DOI 2007
Cognition, Object detection, Deformable models, Mixture models, Detectors, Pattern matching, Adaptation models, Faster R-CNN BibRef

He, X., Bai, S., Chu, J., Bai, X.,
An Improved Multi-View Convolutional Neural Network for 3D Object Retrieval,
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IEEE DOI 2007
Shape, Feature extraction, Measurement, Training, Convolutional neural networks, Task analysis, multi-view CNN BibRef

Xu, D.L.[Dong-Li], Guan, J.[Jian], Feng, P.M.[Peng-Ming], Wang, W.W.[Wen-Wu],
Association Loss for Visual Object Detection,
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IEEE DOI 2009
Object detection, Detectors, Training, Heating systems, Feature extraction, Visualization, Convolutional neural networks, convolutional neural networks BibRef

Chen, C., Zhang, Y., Lv, Q., Wei, S., Wang, X., Sun, X., Dong, J.,
RRNet: A Hybrid Detector for Object Detection in Drone-Captured Images,
VisDrone19(100-108)
IEEE DOI 2004
image capture, learning (artificial intelligence), neural nets, object detection, regression analysis, Deep Learning BibRef

Zhang, R.Q.[Rui-Qian], Shao, Z.F.[Zhen-Feng], Huang, X.[Xiao], Wang, J.M.[Jia-Ming], Li, D.R.[De-Ren],
Object Detection in UAV Images via Global Density Fused Convolutional Network,
RS(12), No. 19, 2020, pp. xx-yy.
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Chen, S., Li, Z., Tang, Z.,
Relation R-CNN: A Graph Based Relation-Aware Network for Object Detection,
SPLetters(27), 2020, pp. 1680-1684.
IEEE DOI 1806
Semantics, Object detection, Proposals, Automobiles, Detectors, Visualization, Feature extraction, Object detection, spatial relation BibRef

Guo, W.[Wei], Li, W.H.[Wei-Hong], Li, Z.H.[Zheng-Hao], Gong, W.G.[Wei-Guo], Cui, J.K.[Jin-Kai], Wang, X.R.[Xin-Ran],
A Slimmer Network with Polymorphic and Group Attention Modules for More Efficient Object Detection in Aerial Images,
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Deng, S.T.[Su-Tao], Li, S.A.[Shu-Ai], Xie, K.[Ke], Song, W.F.[Wen-Feng], Liao, X.[Xiao], Hao, A.M.[Ai-Min], Qin, H.[Hong],
A Global-Local Self-Adaptive Network for Drone-View Object Detection,
IP(30), 2021, pp. 1556-1569.
IEEE DOI 2101
Detectors, Object detection, Training, Training data, Proposals, Feature extraction, Convolution, Drone-view object detection, coarse-to-fine adaptive detector BibRef

Zhang, S., Wen, L., Lei, Z., Li, S.Z.,
RefineDet++: Single-Shot Refinement Neural Network for Object Detection,
CirSysVideo(31), No. 2, February 2021, pp. 674-687.
IEEE DOI 2102
Object detection, Feature extraction, Detectors, Convolution, Proposals, Neural networks, Training, Object detection, one-stage, refinement network BibRef

Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.,
Single-Shot Refinement Neural Network for Object Detection,
CVPR18(4203-4212)
IEEE DOI 1812
Object detection, Detectors, Feature extraction, Task analysis, Training, Convolution, Search problems BibRef

Hassanzadeh, T.[Tahereh], Essam, D.[Daryl], Sarker, R.[Ruhul],
2D to 3D Evolutionary Deep Convolutional Neural Networks for Medical Image Segmentation,
MedImg(40), No. 2, February 2021, pp. 712-721.
IEEE DOI 2102
BibRef
Earlier:
Evolutionary Attention Network for Medical Image Segmentation,
DICTA20(1-8)
IEEE DOI 2201
Biomedical imaging, Image segmentation, Encoding, neuroevolution. Training, Network topology, Neural networks, Task analysis, Genetic algorithms BibRef

Sun, X.[Xian], Wang, P.J.[Pei-Jin], Wang, C.[Cheng], Liu, Y.F.[Ying-Fei], Fu, K.[Kun],
PBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery,
PandRS(173), 2021, pp. 50-65.
Elsevier DOI 2102
Object detection, Remote sensing imagery, Complex composite object, Part-based detection, Context information BibRef

Chen, X., Li, H., Wu, Q., Ngan, K.N., Xu, L.,
High-Quality R-CNN Object Detection Using Multi-Path Detection Calibration Network,
CirSysVideo(31), No. 2, February 2021, pp. 715-727.
IEEE DOI 2102
Proposals, Detectors, Feature extraction, Calibration, Object detection, Benchmark testing, Neural networks, object recognition BibRef

Song, L.Y.[Ling-Yun], Liu, J.[Jun], Sun, M.X.[Ming-Xuan], Shang, X.Q.[Xue-Qun],
Weakly Supervised Group Mask Network for Object Detection,
IJCV(129), No. 3, March 2021, pp. 681-702.
Springer DOI 2103
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Huang, W.[Wei], Li, G.Y.[Guan-Yi], Chen, Q.Q.[Qi-Qiang], Ju, M.[Ming], Qu, J.T.[Jian-Tao],
CF2PN: A Cross-Scale Feature Fusion Pyramid Network Based Remote Sensing Target Detection,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
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Li, Z.H.[Zhi-Hang], Xi, T.[Teng], Zhang, G.[Gang], Liu, J.T.[Jing-Tuo], He, R.[Ran],
AutoDet: Pyramid Network Architecture Search for Object Detection,
IJCV(129), No. 4, April 2021, pp. 1087-1105.
Springer DOI 2104
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Cai, Z.W.[Zhao-Wei], Vasconcelos, N.M.[Nuno M.],
Cascade R-CNN: High Quality Object Detection and Instance Segmentation,
PAMI(43), No. 5, May 2021, pp. 1483-1498.
IEEE DOI 2104
BibRef
Earlier:
Cascade R-CNN: Delving Into High Quality Object Detection,
CVPR18(6154-6162)
IEEE DOI 1812
Detectors, Object detection, Training, Proposals, Task analysis, Feature extraction, Object detection, instance segmentation. Noise measurement BibRef

Sharma, V.[Vipul], Mir, R.N.[Roohie Naaz],
Maximum entropy-based semi-supervised learning for automatic detection and recognition of objects using deep ConvNets,
IJCVR(11), No. 3, 2021, pp. 328-356.
DOI Link 2106
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Shuang, K.[Kai], Lyu, Z.H.[Zhi-Heng], Loo, J.[Jonathan], Zhang, W.T.[Wen-Tao],
Scale-balanced loss for object detection,
PR(117), 2021, pp. 107997.
Elsevier DOI 2106
Object detection, Neural network, Matching imbalance BibRef

Jiang, Y.Y.[Yin-Yin], Li, M.[Ming], Zhang, P.[Peng], Tan, X.F.[Xiao-Feng], Song, W.Y.[Wan-Ying],
Hierarchical fusion convolutional neural networks for SAR image segmentation,
PRL(147), 2021, pp. 115-123.
Elsevier DOI 2106
Synthetic aperture radar, Image segmentation, Hierarchical fusion convolutional neural networks, Dempster-Shafer evidential theory BibRef

Girum, K.B.[Kibrom Berihu], Créhange, G.[Gilles], Lalande, A.[Alain],
Learning With Context Feedback Loop for Robust Medical Image Segmentation,
MedImg(40), No. 6, June 2021, pp. 1542-1554.
IEEE DOI 2106
Image segmentation, Feature extraction, Biomedical imaging, Shape, Decoding, Computed tomography, Feedback loop, CNN, feedback loop, MRI, CT BibRef

Zhu, B.[Bin], Song, Q.[Qing], Yang, L.[Lu], Wang, Z.H.[Zhi-Hui], Liu, C.[Chun], Hu, M.J.[Meng-Jie],
CPM R-CNN: Calibrating Point-guided Misalignment in Object Detection,
WACV21(3247-3256)
IEEE DOI 2106
Location awareness, Heating systems, Estimation, Object detection, Feature extraction BibRef

Wang, K.[Kun], Liu, M.Z.[Mao-Zhen],
A feature-optimized Faster regional convolutional neural network for complex background objects detection,
IET-IPR(15), No. 2, 2021, pp. 378-392.
DOI Link 2106
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Wang, Q.[Qiang], Zhou, L.[Lukuan], Yao, Y.C.[Yun-Cong], Wang, Y.[Yong], Li, J.[Jun], Yang, W.K.[Wan-Kou],
An Interconnected Feature Pyramid Networks for object detection,
JVCIR(79), 2021, pp. 103260.
Elsevier DOI 2109
Attention mechanism, Feature Pyramid Networks, Object detection, Deep learning BibRef

Zhang, L.[Lei], Wang, Y.H.[Yue-Huan], Huo, Y.[Yang],
Object Detection in High-Resolution Remote Sensing Images Based on a Hard-Example-Mining Network,
GeoRS(59), No. 10, October 2021, pp. 8768-8780.
IEEE DOI 2109
Feature extraction, Object detection, Proposals, Remote sensing, Training, Detectors, Task analysis, remote sensing images (RSIs) BibRef

Aziz, L.[Lubna], FC, M.S.B.[Md. Sah Bin_Haji_Salam], Ayub, S.[Sara],
Multi-level refinement enriched feature pyramid network for object detection,
IVC(115), 2021, pp. 104287.
Elsevier DOI 2110
CNN, Object detection, Chained parallel pooling, Feature pyramid BibRef

Li, Z.W.[Zhang-Wei], Hu, A.[Anshun], Wang, X.F.[Xiao-Fei], Hu, J.[Jun], Zhang, G.J.[Gui-Jun],
Learning to capture dependencies between global features of different convolution layers,
JVCIR(81), 2021, pp. 103360.
Elsevier DOI 2112
Deep learning, Object detection, Non-local neural network, Global features dependencies BibRef

Wang, D.[Di], Du, B.[Bo], Zhang, L.P.[Liang-Pei],
Fully Contextual Network for Hyperspectral Scene Parsing,
GeoRS(60), 2022, pp. 1-16.
IEEE DOI 2112
Feature extraction, Hyperspectral imaging, Convolution, Task analysis, Recurrent neural networks, scale attention module (SAM) BibRef

Zhao, J.[Junhe], Xu, S.[Sheng], Wang, R.[Runqi], Zhang, B.C.[Bao-Chang], Guo, G.D.[Guo-Dong], Doermann, D.[David], Sun, D.[Dianmin],
Data-adaptive binary neural networks for efficient object detection and recognition,
PRL(153), 2022, pp. 239-245.
Elsevier DOI 2201
Deep learning, Model compression, Binary neural networks, Object detection, Object recognition BibRef

Tian, Z.[Zhi], Shen, C.H.[Chun-Hua], Chen, H.[Hao], He, T.[Tong],
FCOS: A Simple and Strong Anchor-Free Object Detector,
PAMI(44), No. 4, April 2022, pp. 1922-1933.
IEEE DOI 2203
Detectors, Task analysis, Object detection, Training, Head, Magnetic heads, Semantics, Object detection, deep learning BibRef

Shao, C.Y.[Chun-Yan], Zhang, L.M.[Li-Min], Pan, W.[Wang],
Faster R-CNN Learning-Based Semantic Filter for Geometry Estimation and Its Application in vSLAM Systems,
ITS(23), No. 6, June 2022, pp. 5257-5266.
IEEE DOI 2206
Semantics, Geometry, Estimation, Feature extraction, Visualization, Task analysis, Epipolar geometry, computer vision system, semantic filter BibRef

Liu, H.Z.[Hong-Zhe], Wang, N.W.[Ning-Wei], Li, X.W.[Xue-Wei], Xu, C.[Cheng], Li, Y.Z.[Ya-Ze],
BFF R-CNN: Balanced Feature Fusion for Object Detection,
IEICE(E105-D), No. 8, August 2022, pp. 1472-1480.
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Zheng, Y.C.[Yu-Chao], Zhang, X.X.[Xin-Xin], Zhang, R.[Rui], Wang, D.[Dahan],
Gated Path Aggregation Feature Pyramid Network for Object Detection in Remote Sensing Images,
RS(14), No. 18, 2022, pp. xx-yy.
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Bo, Q.[Qihan], Ma, W.[Wei], Lai, Y.K.[Yu-Kun], Zha, H.B.[Hong-Bin],
All-Higher-Stages-In Adaptive Context Aggregation for Semantic Edge Detection,
CirSysVideo(32), No. 10, October 2022, pp. 6778-6791.
IEEE DOI 2210
Semantics, Image edge detection, Feature extraction, Open systems, Image segmentation, Horses, Aggregates, Semantic edge detection, object-level semantic integration BibRef

Zhang, X.W.[Xiu-Wei], Guo, W.[Wei], Xing, Y.H.[Ying-Hui], Wang, W.[Wenna], Yin, H.L.[Han-Lin], Zhang, Y.N.[Yan-Ning],
AugFCOS: Augmented fully convolutional one-stage object detection network,
PR(134), 2023, pp. 109098.
Elsevier DOI 2212
Feature pyramid network, Object detection, Sample selection, Attention module BibRef

Liang, T.T.[Ting-Ting], Chu, X.J.[Xiao-Jie], Liu, Y.D.[Yu-Dong], Wang, Y.T.[Yong-Tao], Tang, Z.[Zhi], Chu, W.[Wei], Chen, J.D.[Jing-Dong], Ling, H.B.[Hai-Bin],
CBNet: A Composite Backbone Network Architecture for Object Detection,
IP(31), 2022, pp. 6893-6906.
IEEE DOI 2212
Detectors, Object detection, Feature extraction, Computer architecture, Training, Transformers, Head, Deep learning, composite architectures BibRef

Chen, J.J.[Juan-Juan], Hong, H.S.[Han-Sheng], Song, B.[Bin], Guo, J.[Jie], Chen, C.[Chen], Xu, J.J.[Jun-Jie],
MDCT: Multi-Kernel Dilated Convolution and Transformer for One-Stage Object Detection of Remote Sensing Images,
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He, Z.W.[Zhen-Wei], Zhang, L.[Lei], Gao, X.B.[Xin-Bo], Zhang, D.[David],
Multi-adversarial Faster-RCNN with Paradigm Teacher for Unrestricted Object Detection,
IJCV(131), No. 3, March 2023, pp. 680-700.
Springer DOI 2302
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Multi-Adversarial Faster-RCNN for Unrestricted Object Detection,
ICCV19(6667-6676)
IEEE DOI 2004
convolutional neural nets, feature extraction, image classification, object detection, object detection, Training BibRef

Lahoti, G.[Geet], Ranjan, C.[Chitta], Chen, J.[Jialei], Yan, H.[Hao], Zhang, C.[Chuck],
Convolutional Neural Network-Assisted Adaptive Sampling for Sparse Feature Detection in Image and Video Data,
IEEE_Int_Sys(38), No. 1, January 2023, pp. 45-57.
IEEE DOI 2303
Interviews, Feature extraction, Convolutional neural networks, Adaptation models, Visualization, Intelligent systems, Convolutional Neural Network BibRef

Lang, Q.H.[Qing-Hai], Zhang, L.[Lei], Shi, W.X.[Wen-Xu], Chen, W.J.[Wei-Jie], Pu, S.L.[Shi-Liang],
Exploring Implicit Domain-Invariant Features for Domain Adaptive Object Detection,
CirSysVideo(33), No. 4, April 2023, pp. 1816-1826.
IEEE DOI 2304
Feature extraction, Detectors, Object detection, Dams, Automobiles, Training, Transfer learning, Domain adaptation, object detection, transfer learning BibRef

Lang, Q.H.[Qing-Hai], He, Z.W.[Zhen-Wei], Fu, X.W.[Xiao-Wei], Zhang, L.[Lei],
Class-aware Memory Guided Unbiased Weighting for Universal Domain Adaptive Object Detection,
OutDistri23(4347-4356)
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He, Z.W.[Zhen-Wei], Zhang, L.[Lei],
Domain Adaptive Object Detection via Asymmetric Tri-Way Faster-RCNN,
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Wang, S.Y.[Sheng-Ye], Qu, Z.[Zhong],
Multiscale anchor box and optimized classification with faster R-CNN for object detection,
IET-IPR(17), No. 5, 2023, pp. 1322-1333.
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image processing, image recognition, object detection BibRef

Sunkara, R.[Raja], Luo, T.[Tie],
YOGA: Deep object detection in the wild with lightweight feature learning and multiscale attention,
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Chen, L.[Lei], Cao, T.Y.[Tie-Yong], Zheng, Y.F.[Yun-Fei], Fang, Z.[Zheng],
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convolutional neural nets, image segmentation BibRef

Xie, J.[Jin], Pang, Y.W.[Yan-Wei], Nie, J.[Jing], Cao, J.[Jiale], Han, J.G.[Jun-Gong],
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IEEE DOI 2306
Feature extraction, Detectors, Object detection, Convolution, Neural networks, Proposals, Computational modeling, object detection BibRef

Liu, S.H.[Shao-Hua], Yang, A.[Ao], She, C.D.[Chun-Dong], Du, K.[Kang],
Object detection network based on dense dilated encoder net,
IET-IPR(17), No. 9, 2023, pp. 2640-2648.
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centerNet, deep learning, dilated convolution, feature fusion BibRef

Zhao, Z.P.[Zuo-Peng], Hao, K.[Kai], Liu, X.F.[Xiao-Feng], Zheng, T.[Tianci], Xu, J.J.[Jun-Jie], Cui, S.Y.[Shu-Ya], He, C.[Chen], Zhou, J.[Jie], Zhao, G.M.[Guang-Ming],
MCANet: Hierarchical cross-fusion lightweight transformer based on multi-ConvHead attention for object detection,
IVC(136), 2023, pp. 104715.
Elsevier DOI 2308
Object detection, Feature fusion, Transformer, Attention mechanism BibRef

Shen, Y.Y.[Yan-Yun], Liu, D.[Di], Chen, J.Y.[Jun-Yi], Wang, Z.P.[Zhi-Pan], Wang, Z.[Zhe], Zhang, Q.L.[Qing-Ling],
On-Board Multi-Class Geospatial Object Detection Based on Convolutional Neural Network for High Resolution Remote Sensing Images,
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Wen, J.Z.[Jia-Zheng], Liu, H.Y.[Huan-Yu], Li, J.[Junbao],
A Task-Risk Consistency Object Detection Framework Based on Deep Reinforcement Learning,
RS(15), No. 20, 2023, pp. 5031.
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Sun, P.Z.[Pei-Ze], Zhang, R.F.[Ru-Feng], Jiang, Y.[Yi], Kong, T.[Tao], Xu, C.F.[Chen-Feng], Zhan, W.[Wei], Tomizuka, M.[Masayoshi], Yuan, Z.H.[Ze-Huan], Luo, P.[Ping],
Sparse R-CNN: An End-to-End Framework for Object Detection,
PAMI(45), No. 12, December 2023, pp. 15650-15664.
IEEE DOI 2311
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Sun, P.Z.[Pei-Ze], Zhang, R.F.[Ru-Feng], Jiang, Y.[Yi], Kong, T.[Tao], Xu, C.F.[Chen-Feng], Zhan, W.[Wei], Tomizuka, M.[Masayoshi], Li, L.[Lei], Yuan, Z.H.[Ze-Huan], Wang, C.H.[Chang-Hu], Luo, P.[Ping],
Sparse R-CNN: End-to-End Object Detection with Learnable Proposals,
CVPR21(14449-14458)
IEEE DOI 2111
Training, Schedules, Head, Object detection, Detectors, Proposals BibRef

Cao, Y.H.[Yun-Hao], Wu, J.X.[Jian-Xin],
Tobias: A Random CNN Sees Objects,
PAMI(46), No. 2, February 2024, pp. 1290-1304.
IEEE DOI 2401
randomly initialized networks, object localization, self-supervised learning BibRef

Mai, S.H.[Shu-Hua], You, Y.[Yanan], Feng, Y.[Yunxiang],
SGR: An Improved Point-Based Method for Remote Sensing Object Detection via Dual-Domain Alignment Saliency-Guided RepPoints,
RS(16), No. 2, 2024, pp. 250.
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Xie, X.[Xin], Wu, D.Q.[Deng-Quan], Xie, M.[Mingye], Li, Z.X.[Zi-Xi],
GhostFormer: Efficiently amalgamated CNN-transformer architecture for object detection,
PR(148), 2024, pp. 110172.
Elsevier DOI 2402
Object detection, Lightweight network design, Feature extraction, CNN-transformer BibRef

Mabon, J.[Jules], Ortner, M.[Mathias], Zerubia, J.[Josiane],
Learning Point Processes and Convolutional Neural Networks for Object Detection in Satellite Images,
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Bayes R-CNN: An Uncertainty-Aware Bayesian Approach to Object Detection In Remote Sensing Imagery for Enhanced Scene Interpretation,
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Battish, N.[Neeraj], Kaur, D.[Dapinder], Chugh, M.[Moksh], Poddar, S.[Shashi],
SDMNet: Spatially dilated multi-scale network for object detection for drone aerial imagery,
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Elsevier DOI 2409
Object detection, Aerial imaging, Deep learning, Attention modeling, Multi-scaling BibRef

Chen, P.[Ping], Zhang, X.[Xinapeng], Zhou, C.T.[Cheng-Tao], Fan, D.[Dichao], Tu, P.[Peng], Zhang, L.[Le], Qian, Y.L.[Yan-Lin],
Learning Triangular Distribution in Visual World,
CVPR24(11019-11029)
IEEE DOI Code:
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Experiments on Facial Age Recognition, Illumination Chromaticity Estimation, and Aesthetics assessment. Visualization, Histograms, Art, Image color analysis, Shape, Face recognition, Pose estimation BibRef

Fan, C.L.[Ching-Lung],
Multiscale Feature Extraction by Using Convolutional Neural Network: Extraction of Objects from Multiresolution Images of Urban Areas,
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Wang, S.[Shuai], Teng, Y.[Yao], Wang, L.M.[Li-Min],
Deep Equilibrium Object Detection,
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IEEE DOI 2401
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Vierling, A.[Axel], Chawda, A.[Ajay], Manjunath, M.K.B.[Mahesh Kashyap Belakavadi], Berns, K.[Karsten],
Quantifiable Robustness Estimation for Object Detection with CNNs Using Intrinsic Dimensionality,
ICIP23(1605-1609)
IEEE DOI 2312
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Du, B.[Bowei], Huang, Y.[Yecheng], Chen, J.X.[Jia-Xin], Huang, D.[Di],
Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images,
CVPR23(13435-13444)
IEEE DOI 2309
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Riedlinger, T.[Tobias], Rottmann, M.[Matthias], Schubert, M.[Marius], Gottschalk, H.[Hanno],
Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors,
WACV23(3910-3920)
IEEE DOI 2302
Uncertainty, Monte Carlo methods, Object detection, Detectors, Probabilistic logic BibRef

Huang, H.[Hao], Li, L.L.[Liang-Liang], Ma, H.B.[Hong-Bing],
An Improved Cascade R-CNN-Based Target Detection Algorithm for UAV Aerial Images,
ICIVC22(232-237)
IEEE DOI 2301
Photography, Location awareness, Image recognition, Target recognition, Fuses, Object detection, cascade detection BibRef

Cheng, Z.[Zhen], Xiong, J.S.[Jian-She], Yang, P.C.[Peng-Cheng], Yang, K.[Kai], Chen, Y.N.[Yun-Nuo],
Object Detection in Optical Remote Sensing Images Based on Improved Lightweight Neural Network,
ICIVC22(152-157)
IEEE DOI 2301
Neural networks, Transfer learning, Transportation, Object detection, Optical imaging, Feature extraction, transfer learning BibRef

Guo, N.[Nan], Luan, S.[Sike], Li, J.Y.[Jing-Yuan],
An Optimization Scheme of Object Detection Model Based on CNN Feature Visualization Method,
ICIVC22(94-99)
IEEE DOI 2301
Training, Visualization, Analytical models, Computational modeling, Object detection, Feature extraction, object detection, YOLOv3, feature visualization BibRef

Jung, H.[Harim], Oh, M.S.[Myeong-Seok], Yang, C.J.[Cheol-Jong], Lee, S.W.[Seong-Whan],
Neural Architecture Adaptation for Object Detection by Searching Channel Dimensions and Mapping Pre-trained Parameters,
ICPR22(2393-2400)
IEEE DOI 2212
Training, Costs, Object detection, Search problems, Task analysis BibRef

Liu, Z.[Zhuang], Mao, H.Z.[Han-Zi], Wu, C.Y.[Chao-Yuan], Feichtenhofer, C.[Christoph], Darrell, T.J.[Trevor J.], Xie, S.N.[Sai-Ning],
A ConvNet for the 2020s,
CVPR22(11966-11976)
IEEE DOI 2210
Image segmentation, Visualization, Computational modeling, Scalability, Semantics, Transformers, Representation learning BibRef

Hong, Q.H.[Qing-Hang], Liu, F.M.[Feng-Ming], Li, D.[Dong], Liu, J.[Ji], Tian, L.[Lu], Shan, Y.[Yi],
Dynamic Sparse R-CNN,
CVPR22(4713-4722)
IEEE DOI 2210
Training, Convolution, Heuristic algorithms, Object detection, Detectors, Transformers, Prediction algorithms, Optimization methods BibRef

Nguyen, D.K.[Duy-Kien], Ju, J.H.[Ji-Hong], Booij, O.[Olaf], Oswald, M.R.[Martin R.], Snoek, C.G.M.[Cees G. M.],
BoxeR: Box-Attention for 2D and 3D Transformers,
CVPR22(4763-4772)
IEEE DOI 2210
Codes, Object detection, Transformers, Task analysis, Recognition: detection, categorization, retrieval, grouping and shape analysis BibRef

Li, Y.L.[Ya-Li], Wang, S.J.[Sheng-Jin],
R(Det)2: Randomized Decision Routing for Object Detection,
CVPR22(4815-4824)
IEEE DOI 2210
Deep learning, Representation learning, Head, Statistical analysis, Neural networks, Object detection, Routing, Recognition: detection, Statistical methods BibRef

Liu, Y.[Yanan], Lu, Y.[Yao],
On-Sensor Binarized Fully Convolutional Neural Network for Localisation and Coarse Segmentation,
EVW22(3628-3637)
IEEE DOI 2210
Performance evaluation, Heating systems, Convolution, Neural networks, Object segmentation, Parallel processing BibRef

Wu, W.[Wei], Chang, H.[Hao], Zheng, Y.H.[Yong-Hua], Li, Z.[Zhu], Chen, Z.W.[Zhi-Wen], Zhang, Z.H.[Zi-Heng],
Contrastive Learning-based Robust Object Detection under Smoky Conditions,
UG22(4294-4301)
IEEE DOI 2210
Training, Object detection, Detectors, Transforms, Prediction algorithms BibRef

Cygert, S.[Sebastian], Czyzewski, A.[Andrzej],
Robust Object Detection with Multi-input Multi-output Faster R-CNN,
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Springer DOI 2205
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Xie, X.X.[Xing-Xing], Cheng, G.[Gong], Wang, J.B.[Jia-Bao], Yao, X.W.[Xi-Wen], Han, J.W.[Jun-Wei],
Oriented R-CNN for Object Detection,
ICCV21(3500-3509)
IEEE DOI 2203
Head, Codes, Refining, Object detection, Detectors, Benchmark testing, Detection and localization in 2D and 3D, BibRef

Azam, B.[Basim], Mandal, R.[Ranju], Verma, B.[Brijesh],
Fully Convolutional Neural Network with Relation Aware Context Information for Image Parsing,
DICTA21(01-06)
IEEE DOI 2201
Image segmentation, Adaptation models, Digital images, Semantics, Neural networks, Image parsing BibRef

Zhu, X.K.[Xing-Kui], Lyu, S.C.[Shu-Chang], Wang, X.[Xu], Zhao, Q.[Qi],
TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-Captured Scenarios,
VisDrone21(2778-2788)
IEEE DOI 2112
Location awareness, Navigation, Image color analysis, Object detection, Detectors, Transformers, Magnetic heads BibRef

Stäcker, L.[Lukas], Fei, J.C.[Jun-Cong], Heidenreich, P.[Philipp], Bonarens, F.[Frank], Rambach, J.[Jason], Stricker, D.[Didier], Stiller, C.[Christoph],
Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization,
ERCVAD21(1015-1022)
IEEE DOI 2112
Performance evaluation, Deep learning, Runtime, Quantization (signal), Laser radar, Image edge detection BibRef

Yang, Z.[Zuomin], Wang, W.L.[Wan-Li],
An Effective Algorithm for Object Detection Based on Deep Learning,
ICIVC21(26-30)
IEEE DOI 2112
Deep learning, Training, Measurement, Neural networks, Object detection, Detection algorithms, object detection, FIoU, neural network BibRef

Zhang, T.Y.[Tian-Yi], Lin, J.[Jie], Hu, P.[Peng], Zhao, B.[Bin], Aly, M.M.S.[Mohamed M. Sabry],
PSRR-MaxpoolNMS: Pyramid Shifted MaxpoolNMS with Relationship Recovery,
CVPR21(15835-15843)
IEEE DOI 2111
Non-maximum Suppression. CNN detection. Pipelines, Detectors, Object detection, Hardware, Convolutional neural networks BibRef

Yang, L.[Le], Jiang, H.J.[Hao-Jun], Cai, R.J.[Ruo-Jin], Wang, Y.L.[Yu-Lin], Song, S.J.[Shi-Ji], Huang, G.[Gao], Tian, Q.[Qi],
CondenseNet V2: Sparse Feature Reactivation for Deep Networks,
CVPR21(3568-3577)
IEEE DOI 2111
Training, Computational modeling, Object detection, Search problems BibRef

Izquierdo-Cordova, R.[Ramon], Mayol-Cuevas, W.[Walterio],
Filter Distribution Templates in Convolutional Networks for Image Classification Tasks,
LXCV21(1241-1246)
IEEE DOI 2109
Computational modeling, Neural networks, Memory management, Manuals, Data models, Convolutional neural networks BibRef

Kechaou, A.[Amine], Martinez, M.[Manuel], Haurilet, M.[Monica], Stiefelhagen, R.[Rainer],
Detective: An Attentive Recurrent Model for Sparse Object Detection,
ICPR21(5340-5347)
IEEE DOI 2105
Training, Visualization, Recurrent neural networks, Object detection, Detectors, Predictive models, Decoding BibRef

Hyeok, Y.J.[Yoo Jin], Dongsuk, K.[Kum], Won, C.J.[Choi Jun],
ScarfNet: Multi-scale Features with Deeply Fused and Redistributed Semantics for Enhanced Object Detection,
ICPR21(4505-4512)
IEEE DOI 2105
Fuses, Semantics, Detectors, Object detection, Transforms, Benchmark testing, Performance gain BibRef

Chen, S.J.[Sheng-Jia], Li, Z.X.[Zhi-Xin], Huang, F.C.[Fei-Cheng], Zhang, C.L.[Can-Long], Ma, H.F.[Hui-Fang],
Object Detection Using Dual Graph Network,
ICPR21(3280-3287)
IEEE DOI 2105
Knowledge engineering, Visualization, Semantics, Directed graphs, Object detection, Detectors, Feature extraction BibRef

Ji, H.Q.[Hao-Qin], Lu, W.Z.[Wei-Zeng], Shen, L.L.[Lin-Lin],
Backbone Based Feature Enhancement for Object Detection,
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Springer DOI 2103
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Sun, Y., Lin, S., Chen, L.,
A Novel Two-path Backbone Network for Object Detection,
CVIDL20(250-254)
IEEE DOI 2102
feature extraction, learning (artificial intelligence), object detection, two-path backbone network. BibRef

Zhang, H.K.[Hong-Kai], Chang, H.[Hong], Ma, B.P.[Bing-Peng], Wang, N.Y.[Nai-Yan], Chen, X.L.[Xi-Lin],
Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training,
ECCV20(XV:260-275).
Springer DOI 2011
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Mei, R., Wang, H., Men, A.,
Attention-Enhanced And More Balanced R-CNN For Object Detection,
ICIP20(2136-2140)
IEEE DOI 2011
Convolution, Object detection, Proposals, Graphics processing units, Task analysis, Standards, Transforms, criss-cross attention module BibRef

Miao, S.Y.[Shu-Yu], Feng, R.[Rui], Zhang, Y.J.[Yue-Jie],
Representation Reconstruction Head for Object Detection,
ICIP20(1516-1520)
IEEE DOI 2011
Head, Sensitivity, Feature extraction, Iron, Object detection, Convolution, Fuses, Object detection, location sensitivity enhancement representation BibRef

Lu, X.[Xin], Li, Q.Q.[Quan-Quan], Li, B.[Buyu], Yan, J.J.[Jun-Jie],
MimicDet: Bridging the Gap Between One-stage and Two-stage Object Detection,
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Springer DOI 2011
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Wang, T.[Tong], Zhu, Y.S.[You-Song], Zhao, C.Y.[Chao-Yang], Zeng, W.[Wei], Wang, Y.W.[Yao-Wei], Wang, J.Q.[Jin-Qiao], Tang, M.[Ming],
Large Batch Optimization for Object Detection: Training Coco in 12 minutes,
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Springer DOI 2011
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Zhou, D.Z.[Dong-Zhan], Zhou, X.C.[Xin-Chi], Zhang, H.W.[Hong-Wen], Yi, S.[Shuai], Ouyang, W.L.[Wan-Li],
Cheaper Pre-training Lunch: An Efficient Paradigm for Object Detection,
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Amjoud, A.B.[Ayoub Benali], Amrouch, M.[Mustapha],
Convolutional Neural Networks Backbones for Object Detection,
ICISP20(282-289).
Springer DOI 2009
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Ke, W., Zhang, T., Huang, Z., Ye, Q., Liu, J., Huang, D.,
Multiple Anchor Learning for Visual Object Detection,
CVPR20(10203-10212)
IEEE DOI 2008
Detectors, Training, Optimization, Object detection, Maximum likelihood estimation, Linear programming, Visualization BibRef

Liu, J., Hou, Q., Cheng, M., Wang, C., Feng, J.,
Improving Convolutional Networks With Self-Calibrated Convolutions,
CVPR20(10093-10102)
IEEE DOI 2008
Convolutional codes, Task analysis, Kernel, Calibration, Standards, Object detection BibRef

Singh, S., Krishnan, S.,
Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks,
CVPR20(11234-11243)
IEEE DOI 2008
Training, Degradation, Neural networks, Object detection, Task analysis, Testing BibRef

Cai, Q., Pan, Y., Wang, Y., Liu, J., Yao, T., Mei, T.,
Learning a Unified Sample Weighting Network for Object Detection,
CVPR20(14161-14170)
IEEE DOI 2008
Detectors, Task analysis, Object detection, Training, Uncertainty, Proposals, Training data BibRef

Joung, S., Kim, S., Kim, H., Kim, M., Kim, I., Cho, J., Sohn, K.,
Cylindrical Convolutional Networks for Joint Object Detection and Viewpoint Estimation,
CVPR20(14151-14160)
IEEE DOI 2008
Feature extraction, Estimation, Kernel, Object recognition, Object detection BibRef

Guo, C., Fan, B., Zhang, Q., Xiang, S., Pan, C.,
AugFPN: Improving Multi-Scale Feature Learning for Object Detection,
CVPR20(12592-12601)
IEEE DOI 2008
Feature extraction, Detectors, Semantics, Object detection, Proposals, Convolution, Data mining BibRef

Wang, A.T.[Ang-Tian], Sun, Y.H.[Yi-Hong], Kortylewski, A., Yuille, A.L.,
Robust Object Detection Under Occlusion With Context-Aware CompositionalNets,
CVPR20(12642-12651)
IEEE DOI 2008
Robustness, Object detection, Machine learning, Estimation, Task analysis, Convolutional neural networks, Context modeling BibRef

Han, Y., Liu, X., Sheng, Z., Ren, Y., Han, X., You, J., Liu, R., Luo, Z.,
Wasserstein Loss based Deep Object Detection,
AutoDrive20(4299-4305)
IEEE DOI 2008
Object detection, Detectors, Feature extraction, Task analysis, Proposals, Measurement, Machine learning BibRef

Li, Y., Chen, Y., Wang, N., Zhang, Z.,
Scale-Aware Trident Networks for Object Detection,
ICCV19(6053-6062)
IEEE DOI 2004
Code, Object Detection.
WWW Link. feature extraction, learning (artificial intelligence), neural nets, Computer architecture BibRef

Li, P., Chen, B., Ouyang, W., Wang, D., Yang, X., Lu, H.,
GradNet: Gradient-Guided Network for Visual Object Tracking,
ICCV19(6161-6170)
IEEE DOI 2004
convolutional neural nets, feature extraction, gradient methods, object detection, object tracking, visual object tracking, Real-time systems BibRef

Xu, H., Yao, L., Li, Z., Liang, X., Zhang, W.,
Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification,
ICCV19(6648-6657)
IEEE DOI 2004
gradient methods, image classification, object detection, NAS, CNN architecture, image classification, Training BibRef

Yang, K., Li, D., Dou, Y.,
Towards Precise End-to-End Weakly Supervised Object Detection Network,
ICCV19(8371-8380)
IEEE DOI 2004
learning (artificial intelligence), object detection, regression analysis, object position prediction, local minima, BibRef

Peng, J.R.[Jun-Ran], Sun, M.[Ming], Zhang, Z.X.[Zhao-Xiang], Tan, T.N.[Tie-Niu], Yan, J.J.[Jun-Jie],
POD: Practical Object Detection With Scale-Sensitive Network,
ICCV19(9606-9615)
IEEE DOI 2004
image filtering, learning (artificial intelligence), object detection, optimisation, practical object detection, Optimization BibRef

Tian, Z., Shen, C., Chen, H., He, T.,
FCOS: Fully Convolutional One-Stage Object Detection,
ICCV19(9626-9635)
IEEE DOI 2004
Code, Object Detection.
WWW Link. image segmentation, object detection, predefined anchor boxes, final detection performance, pre-defined set, anchor box free, Head BibRef

Nie, J., Anwer, R.M., Cholakkal, H., Khan, F.S., Pang, Y., Shao, L.,
Enriched Feature Guided Refinement Network for Object Detection,
ICCV19(9536-9545)
IEEE DOI 2004
Code, Object Detection.
WWW Link. feature extraction, image classification, learning (artificial intelligence), neural nets, Benchmark testing BibRef

Aghdam, H.H., Gonzalez-Garcia, A., Lopez, A., Weijer, J.,
Active Learning for Deep Detection Neural Networks,
ICCV19(3671-3679)
IEEE DOI 2004
learning (artificial intelligence), neural nets, object detection, pedestrians, active learning, BibRef

Zeng, Z., Liu, B., Fu, J., Chao, H., Zhang, L.,
WSOD2: Learning Bottom-Up and Top-Down Objectness Distillation for Weakly-Supervised Object Detection,
ICCV19(8291-8299)
IEEE DOI 2004
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Rahman, S., Khan, S., Barnes, N.,
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Object Detection, Deep Learning, CNN, Network Pruning, Clustering BibRef

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image annotation, object detection, unsupervised learning, video signal processing, unannotated video dataset, Image color analysis BibRef

Ebrahimpour, M.K., Li, J., Yu, Y., Reesee, J., Moghtaderi, A., Yang, M., Noelle, D.C.,
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WACV19(986-994)
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brain, convolutional neural nets, feature extraction, image classification, neurophysiology, object detection, Training BibRef

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CVPR18(1101-1110)
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Robust UAV Visual Teach and Repeat Using Only Sparse Semantic Object Features,
CRV18(182-189)
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ICIP18(3443-3447)
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Resilience, Convolution, Training, Object detection, Training data, Stress, Detectors, Deep learning resilience, Network self healing BibRef

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ICIP18(3488-3492)
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Shape, Image segmentation, Training, Databases, Adaptation models, Solid modeling, Adaptive shape prior, MGRF BibRef

Choi, M., Park, J., Jung, J., Jung, H., Lee, J., Won, W., Jung, W.Y., Kim, J., Kwon, S.,
Co-Occurrence Matrix Analysis-Based Semi-Supervised Training for Object Detection,
ICIP18(1333-1337)
IEEE DOI 1809
Training, Detectors, Object detection, Labeling, Neural networks, Testing, Encoding, Object detection, Semi-supervised learning, Co-occurrence matrix BibRef

Tchuinkou, D., Bobda, C.,
R-Covnet: Recurrent Neural Convolution Network for 3D Object Recognition,
ICIP18(331-335)
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Logic gates, Convolution, Object recognition, Data models, 3D Object Recognition BibRef

Zhang, X., Chen, Y., Zhu, B., Wang, J., Tang, M., Lu, H.,
Tree Hierarchical CNNs for Object Parsing,
ICIP18(1588-1592)
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Image segmentation, Head, Semantics, Torso, Visualization, Legged locomotion, Cows, object parsing, tree hierarchical CNNs, part-aware fusion BibRef

Fukagai, T., Maeda, K., Tanabe, S., Shirahata, K., Tomita, Y., Ike, A., Nakagawa, A.,
Speed-Up of Object Detection Neural Network with GPU,
ICIP18(301-305)
IEEE DOI 1809
Graphics processing units, Object detection, Neural networks, Proposals, Sorting, Convolution, Instruction sets, deep learning, GPU BibRef

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Geometric proposals for faster R-CNN,
AVSS17(1-6)
IEEE DOI 1806
convolution, geometry, neural nets, object detection, object tracking, video signal processing, video surveillance, Training BibRef

Hamaguchi, R., Fujita, A., Nemoto, K., Imaizumi, T., Hikosaka, S.,
Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery,
WACV18(1442-1450)
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feature extraction, geophysical image processing, image segmentation, remote sensing, CNNs, LFE module, Spatial resolution BibRef

Ha, M.L.[Mai Lan], Franchi, G.[Gianni], Moller, M.[Michael], Kolb, A.[Andreas], Blanz, V.[Volker],
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WACV18(1509-1518)
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convolution, feature extraction, feedforward neural nets, image classification, image resolution, image segmentation, Training BibRef

Sobti, A., Arora, C., Balakrishnan, M.,
Object Detection in Real-Time Systems: Going Beyond Precision,
WACV18(1020-1028)
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feedforward neural nets, object detection, real-time systems, convolutional neural networks, Robots BibRef

Srivastava, S., Sharma, G., Lall, B.,
Large Scale Novel Object Discovery in 3D,
WACV18(179-188)
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feedforward neural nets, learning (artificial intelligence), object detection, 3D convolutional neural network architecture, Training BibRef

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VCIP17(1-4)
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convolution, feedforward neural nets, image resolution, image segmentation, object detection, regression analysis, Overlap Loss BibRef

Han, G., Zhang, X., Li, C.,
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ICIP17(3360-3364)
IEEE DOI 1803
Convolutional neural networks, Detectors, Feature extraction, Object detection, Proposals, Semantics, Training, top-down refinement BibRef

Li, J.[Jian], Qian, J.J.[Jian-Jun], Yang, J.[Jian],
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ICIP17(3390-3394)
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Guo, Y., Guo, X., Jiang, Z., Zhou, Y.,
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VCIP17(1-4)
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image classification, neural nets, object detection, binary classifier, convolutional neural networks, Training BibRef

Guo, Y., Guo, X., Jiang, Z., Men, A., Zhou, Y.,
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ICIP17(670-674)
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Feature extraction, Microprocessors, Object detection, Proposals, Real-time systems, Semantics, multi-feature BibRef

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ICIP17(680-684)
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Li, T., Zhao, X.,
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ICIP17(4202-4206)
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Automobiles, Benchmark testing, Heating systems, Neurons, Object detection, Proposals, Subcategory BibRef

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DeepSetNet: Predicting Sets with Deep Neural Networks,
ICCV17(5257-5266)
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image classification, learning (artificial intelligence), neural nets, Random variables BibRef

Gadde, R.[Raghudeep], Jampani, V.[Varun], Gehler, P.V.[Peter V.],
Semantic Video CNNs Through Representation Warping,
ICCV17(4463-4472)
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CNN for semantic segmentation into CNN for video data. image sequences, video signal processing, video streaming, Transforms BibRef

Kim, D., Cho, D., Yoo, D.,
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ICCV17(3554-3563)
IEEE DOI 1802
convolution, image annotation, image segmentation, inference mechanisms, learning (artificial intelligence), Training BibRef

Zhou, H., Li, Z., Ning, C., Tang, J.,
CAD: Scale Invariant Framework for Real-Time Object Detection,
AutoRob17(760-768)
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Zhang, R.[Rui], Tang, S.[Sheng], Zhang, Y.D.[Yong-Dong], Li, J.T.[Jin-Tao], Yan, S.C.[Shui-Cheng],
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ICCV17(2050-2058)
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Xu, H., Dong, M., Zhong, Z.,
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ICCV17(2717-2726)
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convolution, image representation, image segmentation, learning (artificial intelligence), shape recognition, BibRef

Liu, Y., Li, H., Yan, J., Wei, F., Wang, X., Tang, X.,
Recurrent Scale Approximation for Object Detection in CNN,
ICCV17(571-579)
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convolution, feature extraction, neural nets, object detection, RSA, convolutional neural network, feature maps, Prediction algorithms BibRef

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Kong, X.Y.[Xiang-Yu], Xin, B.[Bo], Wang, Y.Z.[Yi-Zhou], Hua, G.[Gang],
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CVPR17(7072-7081)
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Bicycles, Collaboration, Learning (artificial intelligence), Logic gates, Object detection, Proposals, Search, problems BibRef

Jetley, S.[Saumya], Sapienza, M.[Michael], Golodetz, S.[Stuart], Torr, P.H.S.[Philip H.S.],
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CVPR17(4207-4216)
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CVPR17(4294-4302)
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SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving,
ECVW17(446-454)
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Kudo, Y.[Yasunori], Aoki, Y.[Yoshimitsu],
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WACV17(934-943)
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Xiang, Y., Choi, W., Lin, Y., Savarese, S.,
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Misra, I.[Ishan], Zitnick, C.L.[C. Lawrence], Mitchell, M.[Margaret], Girshick, R.[Ross],
Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels,
CVPR16(2930-2939)
IEEE DOI 1612
BibRef

Najibi, M., Rastegari, M., Davis, L.S.,
G-CNN: An Iterative Grid Based Object Detector,
CVPR16(2369-2377)
IEEE DOI 1612
BibRef

Borji, A.[Ali], Izadi, S.[Saeed], Itti, L.[Laurent],
iLab-20M: A Large-Scale Controlled Object Dataset to Investigate Deep Learning,
CVPR16(2221-2230)
IEEE DOI 1612
Dataset, Learning. BibRef

Chen, X.Z.[Xiao-Zhi], Kundu, K.[Kaustav], Zhu, Y., Ma, H.M.[Hui-Min], Fidler, S.[Sanja], Urtasun, R.[Raquel],
3D Object Proposals Using Stereo Imagery for Accurate Object Class Detection,
PAMI(40), No. 5, May 2018, pp. 1259-1272.
IEEE DOI 1804
Context, Detectors, Laser radar, Object detection, Proposals, Solid modeling, 3D object detection, stereo BibRef

Chen, X.Z.[Xiao-Zhi], Kundu, K.[Kaustav], Zhang, Z.Y.[Zi-Yu], Ma, H.M.[Hui-Min], Fidler, S.[Sanja], Urtasun, R.[Raquel],
Monocular 3D Object Detection for Autonomous Driving,
CVPR16(2147-2156)
IEEE DOI 1612
BibRef

Hayder, Z., He, X., Salzmann, M.,
Learning to Co-Generate Object Proposals with a Deep Structured Network,
CVPR16(2565-2573)
IEEE DOI 1612
BibRef

Jampani, V.[Varun], Kiefel, M.[Martin], Gehler, P.V.[Peter V.],
Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks,
CVPR16(4452-4461)
IEEE DOI 1612
BibRef

Jampani, V.[Varun], Gadde, R.[Raghudeep], Gehler, P.V.[Peter V.],
Video Propagation Networks,
CVPR17(3154-3164)
IEEE DOI 1711
Image color analysis, Lattices, Optimization, Runtime, Semantics, Virtual, private, networks BibRef

Gadde, R.[Raghudeep], Jampani, V.[Varun], Kiefel, M.[Martin], Kappler, D.[Daniel], Gehler, P.V.[Peter V.],
Superpixel Convolutional Networks Using Bilateral Inceptions,
ECCV16(I: 597-613).
Springer DOI 1611
BibRef

Liu, W.[Wei], Anguelov, D.[Dragomir], Erhan, D.[Dumitru], Szegedy, C.[Christian], Reed, S.[Scott], Fu, C.Y.[Cheng-Yang], Berg, A.C.[Alexander C.],
SSD: Single Shot MultiBox Detector,
ECCV16(I: 21-37).
Springer DOI 1611
BibRef

Cai, Z.W.[Zhao-Wei], He, X., Sun, J., Vasconcelos, N.M.[Nuno M.],
Deep Learning with Low Precision by Half-Wave Gaussian Quantization,
CVPR17(5406-5414)
IEEE DOI 1711
Backpropagation, Biological neural networks, Complexity theory, Computational modeling, Machine learning, Quantization, (signal) BibRef

Cai, Z.W.[Zhao-Wei], Fan, Q.F.[Quan-Fu], Feris, R.S.[Rogerio S.], Vasconcelos, N.M.[Nuno M.],
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection,
ECCV16(IV: 354-370).
Springer DOI 1611
BibRef

Krusch, P., Bochinski, E., Eiselein, V., Sikora, T.,
A consistent two-level metric for evaluation of automated abandoned object detection methods,
ICIP17(4352-4356)
IEEE DOI 1803
BibRef
Earlier: A2, A3, A4, Only:
Training a convolutional neural network for multi-class object detection using solely virtual world data,
AVSS16(278-285)
IEEE DOI 1611
Cameras, Feature extraction, Lighting, Object detection, Protocols, Volume measurement, abandoned object detection, evaluation, metric, video surveillance. Animals BibRef

Cervantes, E., Yu, L.L., Bagdanov, A.D.[Andrew D.], Masana, M., van de Weijer, J.[Joost],
Hierarchical part detection with deep neural networks,
ICIP16(1933-1937)
IEEE DOI 1610
Birds BibRef

Kuo, W., Hariharan, B., Malik, J.,
DeepBox: Learning Objectness with Convolutional Networks,
ICCV15(2479-2487)
IEEE DOI 1602
Computer architecture BibRef

Ma, C.H.[Chih-Hao], Hsu, C.T.[Chiou-Ting], Huet, B.[Benoit],
Nonparametric scene parsing with deep convolutional features and dense alignment,
ICIP15(1915-1919)
IEEE DOI 1512
SIFT flow; deep convolutional network; object window; scene parsing BibRef

Caicedo, J.C., Lazebnik, S.[Svetlana],
Active Object Localization with Deep Reinforcement Learning,
ICCV15(2488-2496)
IEEE DOI 1602
Computational modeling BibRef

Pepik, B.[Bojan], Benenson, R.[Rodrigo], Ritschel, T.[Tobias], Schiele, B.[Bernt],
What Is Holding Back Convnets for Detection?,
GCPR15(517-528).
Springer DOI 1511
BibRef

Mrowca, D., Rohrbach, M.[Marcus], Hoffman, J., Hu, R., Saenko, K., Darrell, T.J.,
Spatial Semantic Regularisation for Large Scale Object Detection,
ICCV15(2003-2011)
IEEE DOI 1602
Clustering algorithms BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Deep Learning, Deep Nets, DNN .