14.5.8.6.9 Convolutional Neural Networks for Object Detection and Segmentation

He, S.F.[Sheng-Feng], Lau, R.W.H.[Rynson W.H.], Liu, W.[Wenxi], Huang, Z.[Zhe], Yang, Q.X.[Qing-Xiong],
SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection,
IJCV(115), No. 3, December 2015, pp. 330-344.
Springer DOI 1512
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Shin, H.C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., Summers, R.M.,
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,
MedImg(35), No. 5, May 2016, pp. 1285-1298.
IEEE DOI 1605
Biomedical imaging BibRef

Ren, S.Q.[Shao-Qing], He, K.M.[Kai-Ming], Girshick, R.[Ross], Zhang, X., Sun, J.[Jian],
Object Detection Networks on Convolutional Feature Maps,
PAMI(39), No. 7, July 2017, pp. 1476-1481.
IEEE DOI 1706
Detectors, Electronic mail, 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],
Robust object representation by boosting-like deep learning architecture,
SP:IC(47), No. 1, 2016, pp. 490-499.
Elsevier DOI 1610
Boosting BibRef

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
convolution BibRef

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

Pang, S.M.[Shan-Min], Zhu, J.H.[Ji-Hua], Wang, J.X.[Jia-Xing], Ordonez, V.[Vicente], Xue, J.R.[Jian-Ru],
Building discriminative CNN image representations for object retrieval using the replicator equation,
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
BibRef
Earlier: A1, A4, A3, Only:
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
BibRef
Earlier:
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

Xiao, F.[Fen], Deng, W.Z.[Wen-Zheng], Peng, L.C.[Liang-Chan], Cao, C.H.[Chun-Hong], Hu, K.[Kai], Gao, X.P.[Xie-Ping],
Multi-scale deep neural network for salient object detection,
IET-IPR(12), No. 11, November 2018, pp. 2036-2041.
DOI Link 1810
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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

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

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, Computer vision, 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.
DOI Link 1902
Annotation for deep learning input. BibRef

Zhu, J.[Jun], Zhu, J.C.[Jiang-Cheng], Wan, X.D.[Xu-Dong], Wu, C.[Chao], Xu, C.[Chao],
Object detection and localization in 3D environment by fusing raw fisheye image and attitude data,
JVCIR(59), 2019, pp. 128-139.
Elsevier DOI 1903
Object detection, Deep learning, Data fusion, Fisheye camera, Micro aerial vehicle, Localization BibRef

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
Earlier:
Unsupervised Object Discovery for Instance Recognition,
WACV18(1745-1754)
IEEE DOI 1806
With background clutter. computer vision, 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,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link 1906
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Hsu, K., Lin, Y., Chuang, Y.,
Weakly Supervised Salient Object Detection by Learning A Classifier-Driven Map Generator,
IP(28), No. 11, November 2019, pp. 5435-5449.
IEEE DOI 1909
Saliency detection, Generators, Training data, Training, Feature extraction, Proposals, Task analysis, weakly supervised learning BibRef

Peng, H.[Hanyu], 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
Computer architecture, 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
computer vision, 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, Computer architecture, Pipelines, Multiple experts, expert assigner BibRef

Lu, Q.S.[Qi-Shuo], Jiang, Z.Q.[Zhu-Qing], Men, A.D.[Ai-Dong], Tang, P.L.[Peng-Liang],
Object detection using convolutional networks with adaptively adjusting receptive field of convolutional filter,
IET-CV(13), No. 6, September 2019, pp. 562-568.
DOI Link 1911
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Zeng, Z.[Zeng], Xulei, Y.[Yang], Qiyun, Y.[Yu], Meng, Y.[Yao], Le, Z.[Zhang],
SeSe-Net: Self-Supervised deep learning for segmentation,
PRL(128), 2019, pp. 23-29.
Elsevier DOI 1912
Self-Supervised learning, Deep learning, Segmentation, U-Net BibRef

Hu, X.G.[Xue-Gang], Yang, H.G.[Hong-Guang],
DRU-net: a novel U-net for biomedical image segmentation,
IET-IPR(14), No. 1, January 2020, pp. 192-200.
DOI Link 1912
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Ouadiay, F.Z.[Fatima Zahra], Zrira, N.[Nabila], Hannat, M.[Mohamed], Bouyakhf, E.[El_Houssine], Himmi, M.M.[Majid Mohamed],
3D object classification based on deep belief networks and point clouds,
IJCVR(9), No. 6, 2019, pp. 527-558.
DOI Link 1912
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Ouadiay, F.Z.[Fatima Zahra], Bouftaih, H., Bouyakhf, E.[El_Houssine], Himmi, M.M.[Majid Mohamed],
Simultaneous object detection and localization using convolutional neural networks,
ISCV18(1-8)
IEEE DOI 1807
computer vision, convolution, feature extraction, feedforward neural nets, image classification, object detection BibRef

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
Earlier:
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

Zhang, Q., Huang, N., Yao, L., Zhang, D., Shan, C., Han, J.,
RGB-T Salient Object Detection via Fusing Multi-Level CNN Features,
IP(29), 2020, pp. 3321-3335.
IEEE DOI 2002
RGB-T salient object detection, adjacent-depth feature combination, multi-branch group fusion, joint attention guided bi-directional message passing 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

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,
SIViP(14), No. 2, March 2020, pp. 425-433.
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],
ASAN: Self-Attending and Semantic Activating Network towards Better Object Detection,
IEICE(E103-D), No. 3, March 2020, pp. 648-659.
WWW Link. 2003
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Feng, M., Lu, H., Yu, Y.,
Residual Learning for Salient Object Detection,
IP(29), 2020, pp. 4696-4708.
IEEE DOI 2003
Object detection, Feature extraction, Image reconstruction, Visualization, Task analysis, Image segmentation, deep learning BibRef

Huang, K.[Kun], Gao, S.H.[Sheng-Hua],
Image saliency detection via multi-scale iterative CNN,
VC(36), No. 7, July 2020, pp. 1355-1367.
WWW Link. 2005
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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.[Yuhan], Ke, C.[Cong], Zeng, C.[Cheng],
Object detection based on RGC mask R-CNN,
IET-IPR(14), No. 8, 19 June 2020, pp. 1502-1508.
DOI Link 2005
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Yuan, J.[Jin], Xiong, H.C.[Heng-Chang], Xiao, Y.[Yi], Guan, W.[Weili], Wang, M.[Meng], Hong, R.[Richang], Li, Z.Y.[Zhi-Yong],
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.,
Occlusion Pattern Discovery for Object Detection and Occlusion Reasoning,
CirSysVideo(30), No. 7, July 2020, pp. 2067-2080.
IEEE DOI 2007
Cognition, Object detection, Deformable models, Mixture models, Detectors, Pattern matching, Adaptation models, Faster R-CNN BibRef

Xie, S.R.[Shao-Rong], Liu, C.[Chang], Gao, J.T.[Jian-Tao], Li, X.M.[Xiao-Mao], Luo, J.[Jun], Fan, B.[Baojie], Chen, J.H.[Jia-Hong], Pu, H.Y.[Hua-Yan], Peng, Y.[Yan],
Diverse receptive field network with context aggregation for fast object detection,
JVCIR(70), 2020, pp. 102770.
Elsevier DOI 2007
Object detection, Convolutional neural network, Context aggregation, Multi-scale contextual representations BibRef

He, X., Bai, S., Chu, J., Bai, X.,
An Improved Multi-View Convolutional Neural Network for 3D Object Retrieval,
IP(29), 2020, pp. 7917-7930.
IEEE DOI 2007
Shape, Feature extraction, Measurement, Training, Convolutional neural networks, Task analysis, multi-view CNN BibRef

Zhang, Q.[Qing], Shi, Y.[Yanjiao], Zhang, X.[Xueqin],
Attention and boundary guided salient object detection,
PR(107), 2020, pp. 107484.
Elsevier DOI 2008
Salient object detection, Visual saliency, Feature learning, Fully convolutional neural network 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,
SPLetters(27), 2020, pp. 1435-1439.
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

Azadi, S.[Samaneh], Pathak, D.[Deepak], Ebrahimi, S.[Sayna], Darrell, T.J.[Trevor J.],
Compositional GAN: Learning Image-Conditional Binary Composition,
IJCV(128), No. 10-11, November 2020, pp. 2570-2585.
Springer DOI 2009
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Azadi, S.[Samaneh], Feng, J.S.[Jia-Shi], Darrell, T.J.[Trevor J.],
Learning Detection with Diverse Proposals,
CVPR17(7369-7377)
IEEE DOI 1711
Computer architecture, Convolutional codes, Kernel, Object detection, Proposals, Semantics, Training BibRef

Liu, C., Zhou, W., Chen, Y., Lei, J.,
Asymmetric Deeply Fused Network for Detecting Salient Objects in RGB-D Images,
SPLetters(27), 2020, pp. 1620-1624.
IEEE DOI 2010
Feature extraction, Decoding, Convolution, Adaptation models, Fuses, Visualization, Object detection, RGB-D, adaptive attention transformer module 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.
DOI Link 2010
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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,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
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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., Essam, D., Sarker, R.,
2D to 3D Evolutionary Deep Convolutional Neural Networks for Medical Image Segmentation,
MedImg(40), No. 2, February 2021, pp. 712-721.
IEEE DOI 2102
Biomedical imaging, Image segmentation, Encoding, neuroevolution 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

Seo, H., Bassenne, M., Xing, L.,
Closing the Gap Between Deep Neural Network Modeling and Biomedical Decision-Making Metrics in Segmentation via Adaptive Loss Functions,
MedImg(40), No. 2, February 2021, pp. 585-593.
IEEE DOI 2102
Training, Neural networks, Measurement, Adaptation models, Decision making, Deep learning, Harmonic analysis, Deep learning, Segmentation 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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Yuan, Z.C.[Zhi-Chao], Liu, Z.M.[Zi-Ming], Zhu, C.B.[Chun-Bo], Qi, J.[Jing], Zhao, D.[Danpei],
Object Detection in Remote Sensing Images via Multi-Feature Pyramid Network with Receptive Field Block,
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.[Nuno],
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, Computer architecture, Feature extraction, Object detection, instance segmentation. Noise measurement BibRef

Fang, W.H.[Wen-Hao], Han, X.H.[Xian-Hua],
Spatial and Channel Attention Modulated Network for Medical Image Segmentation,
MLCSA20(3-17).
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

He, Z.W.[Zhen-Wei], Zhang, L.[Lei],
Domain Adaptive Object Detection via Asymmetric Tri-Way Faster-RCNN,
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Head, Sensitivity, Feature extraction, Iron, Object detection, Convolution, Fuses, Object detection, location sensitivity enhancement representation BibRef

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Liu, J., Hou, Q., Cheng, M., Wang, C., Feng, J.,
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Convolutional codes, Computer architecture, Task analysis, Kernel, Calibration, Standards, Object detection BibRef

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Training, Degradation, Computer architecture, Neural networks, Object detection, Task analysis, Testing BibRef

Cai, Q., Pan, Y., Wang, Y., Liu, J., Yao, T., Mei, T.,
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CVPR20(14161-14170)
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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.,
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CVPR20(14151-14160)
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Feature extraction, Estimation, Kernel, Object recognition, Object detection BibRef

Guo, C., Fan, B., Zhang, Q., Xiang, S., Pan, C.,
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CVPR20(12592-12601)
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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.,
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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.,
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AutoDrive20(4299-4305)
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Object detection, Detectors, Feature extraction, Task analysis, Proposals, Measurement, Machine learning BibRef

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WACV20(942-951)
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Code, Object Detection.
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ICCV19(6053-6062)
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Code, Object Detection.
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Li, P., Chen, B., Ouyang, W., Wang, D., Yang, X., Lu, H.,
GradNet: Gradient-Guided Network for Visual Object Tracking,
ICCV19(6161-6170)
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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)
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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)
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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)
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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)
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Code, Object Detection.
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Nie, J., Anwer, R.M., Cholakkal, H., Khan, F.S., Pang, Y., Shao, L.,
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ICCV19(9536-9545)
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Code, Object Detection.
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Active Learning for Deep Detection Neural Networks,
ICCV19(3671-3679)
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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)
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convolutional neural nets, feature extraction, image representation, learning (artificial intelligence), BibRef

He, Z., Zhang, L.,
Multi-Adversarial Faster-RCNN for Unrestricted Object Detection,
ICCV19(6667-6676)
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convolutional neural nets, feature extraction, image classification, object detection, object detection, Training BibRef

Rahman, S., Khan, S., Barnes, N.,
Transductive Learning for Zero-Shot Object Detection,
ICCV19(6081-6090)
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inference mechanisms, learning (artificial intelligence), object detection, object recognition, unseen objects, inference, Object detection BibRef

Zhang, H., Wang, J.,
Towards Adversarially Robust Object Detection,
ICCV19(421-430)
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neural nets, object detection, unsupervised learning, MS-COCO, PASCAL-VOC, Analytical models BibRef

Wang, T., Anwer, R.M., Cholakkal, H., Khan, F.S., Pang, Y., Shao, L.,
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ICCV19(1971-1980)
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IVCNZ19(1-6)
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convolutional neural nets, learning (artificial intelligence), object detection, convolutional neural networks, Wilding conifers BibRef

Shinya, Y., Simo-Serra, E., Suzuki, T.,
Understanding the Effects of Pre-Training for Object Detectors via Eigenspectrum,
NeruArch19(1931-1941)
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convolutional neural nets, covariance matrices, image classification, object detection, Eigenspectrum BibRef

Gao, J., Wang, J., Dai, S., Li, L., Nevatia, R.,
NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection,
ICCV19(9507-9516)
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data mining, image classification, image representation, image segmentation, learning (artificial intelligence), Standards BibRef

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Object detection, Infrared data fusion, Attention module BibRef

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ICIP19(3915-3919)
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Object Detection, Deep Learning, CNN, Network Pruning, Clustering BibRef

Zhang, C., Cao, Z., Xiong, X., Xian, K., Qi, X.,
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Monocular 3D object detection, convolutional neural networks, autonomous driving, geometric constraints 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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Object detection, Training, Adaptation models, Proposals, Task analysis, Lighting, Feature extraction BibRef

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CVPR18(2694-2703)
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Visualization, Neural networks, Decoding, Image reconstruction, Training, Detectors BibRef

Uijlings, J.R.R., Popov, S., Ferrari, V.,
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CVPR18(1101-1110)
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Proposals, Detectors, Training, Knowledge transfer, Semantics, Neural networks, Standards BibRef

Hua, B., Tran, M., Yeung, S.,
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CVPR18(984-993)
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Li, Y., Zhang, X., Chen, D.,
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CVPR18(1091-1100)
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Womg, A., Shafiee, M.J., Li, F., Chwyl, B.,
Tiny SSD: A Tiny Single-Shot Detection Deep Convolutional Neural Network for Real-Time Embedded Object Detection,
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Object detection, Fires, Microarchitecture, Network architecture, Real-time systems, Neural networks, Feature extraction, single-shot BibRef

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Robust UAV Visual Teach and Repeat Using Only Sparse Semantic Object Features,
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Rahman, F.U., Vasu, B., Savakis, A.,
Resilience and Self-Healing of Deep Convolutional Object Detectors,
ICIP18(3443-3447)
IEEE DOI 1809
Resilience, Convolution, Training, Object detection, Training data, Stress, Detectors, Deep learning resilience, Network self healing BibRef

Soliman, A., Shaffie, A., Ghazal, M., Gimel'farb, G.L.[Georgy L.], Keynton, R., El-Baz, A.,
A Novel CNN Segmentation Framework Based on Using New Shape and Appearance Features,
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)
IEEE DOI 1809
Logic gates, Convolution, Object recognition, Computer architecture, 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)
IEEE DOI 1809
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)
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Graphics processing units, Object detection, Neural networks, Proposals, Sorting, Convolution, Instruction sets, deep learning, GPU BibRef

Amin, S.[Sikandar], Galasso, F.[Fabio],
Geometric proposals for faster R-CNN,
AVSS17(1-6)
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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],
Segmentation and Shape Extraction from Convolutional Neural Networks,
WACV18(1509-1518)
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convolution, feature extraction, feedforward neural nets, image classification, image resolution, image segmentation, Training BibRef

Najibi, M., Yang, F., Wang, Q., Piramuthu, R.,
Towards the Success Rate of One: Real-Time Unconstrained Salient Object Detection,
WACV18(1432-1441)
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Gaussian distribution, convolution, feedforward neural nets, learning (artificial intelligence), object detection, Visualization BibRef

Sobti, A., Arora, C., Balakrishnan, M.,
Object Detection in Real-Time Systems: Going Beyond Precision,
WACV18(1020-1028)
IEEE DOI 1806
computer vision, 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)
IEEE DOI 1806
feedforward neural nets, learning (artificial intelligence), object detection, 3D convolutional neural network architecture, Training BibRef

Zagoruyko, S.[Sergey], Lerer, A.[Adam], Lin, T.Y.[Tsung-Yi], Pinheiro, P.O.[Pedro O.], Gross, S.[Sam], Chintala, S.[Soumith], Dollar, P.[Piotr],
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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.,
Single shot object detection with top-down refinement,
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],
Object detection via feature fusion based single network,
ICIP17(3390-3394)
IEEE DOI 1803
Radio frequency, Dense box, Feature fusion, Hierarchical feature, Object detection, Single network BibRef

Guo, Y., Guo, X., Jiang, Z., Zhou, Y.,
Cascaded convolutional neural networks for object detection,
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.,
Real-time object detection by a multi-feature fully convolutional network,
ICIP17(670-674)
IEEE DOI 1803
Computer architecture, Feature extraction, Microprocessors, Object detection, Proposals, Real-time systems, Semantics, multi-feature BibRef

Etemad, E., Gao, Q.,
Object localization by optimizing convolutional neural network detection score using generic edge features,
ICIP17(675-679)
IEEE DOI 1803
Convolutional neural networks, Image edge detection, Object recognition, Optimization, Proposals, Search problems, RCNN BibRef

Li, H., Yao, H., Hou, Y., Sun, X.,
Gated additive skip context connection for object detection,
ICIP17(680-684)
IEEE DOI 1803
Additives, Computational modeling, Context modeling, Feature extraction, Logic gates, Object detection, Plugs, object detection BibRef

Wan, Z., He, H.,
Weakly supervised object localization with deep convolutional neural network based on spatial pyramid saliency map,
ICIP17(4177-4181)
IEEE DOI 1803
Computer architecture, Computer vision, Convolutional neural networks, Head, Labeling, Machine learning, spatial pyramid saliency map BibRef

Zhang, J., Li, B., Dai, Y., Porikli, F.M.[Fatih M.], He, M.,
Integrated deep and shallow networks for salient object detection,
ICIP17(1537-1541)
IEEE DOI 1803
Color, Convolutional neural networks, Machine learning, Object detection, Saliency detection, Semantics, Training, shallow network BibRef

Li, T., Zhao, X.,
Cost efficient subcategory-aware CNN for object detection,
ICIP17(4202-4206)
IEEE DOI 1803
Automobiles, Benchmark testing, Heating systems, Neurons, Object detection, Proposals, Subcategory BibRef

Rezatofighi, S.H.[S. Hamid], Vijay Kumar, B.G., Milan, A.[Anton], Abbasnejad, M.E.[M. Ehsan], Dick, A.[Anthony], Reid, I.D.[Ian D.],
DeepSetNet: Predicting Sets with Deep Neural Networks,
ICCV17(5257-5266)
IEEE DOI 1802
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)
IEEE DOI 1802
CNN for semantic segmentation into CNN for video data. image sequences, video signal processing, video streaming, Transforms BibRef

Kim, D., Cho, D., Yoo, D.,
Two-Phase Learning for Weakly Supervised Object Localization,
ICCV17(3554-3563)
IEEE DOI 1802
convolution, image annotation, image segmentation, inference mechanisms, learning (artificial intelligence), Training BibRef

Zhu, Y., Zhao, C., Wang, J., Zhao, X., Wu, Y., Lu, H.,
CoupleNet: Coupling Global Structure with Local Parts for Object Detection,
ICCV17(4146-4154)
IEEE DOI 1802
convolution, image classification, neural nets, object detection, Convolutional Neural Network detectors, CoupleNet, Visualization BibRef

Dvornik, N.[Nikita], Mairal, J.[Julien], Schmid, C.[Cordelia],
Modeling Visual Context Is Key to Augmenting Object Detection Datasets,
ECCV18(XII: 375-391).
Springer DOI 1810
BibRef

Dvornik, N.[Nikita], Shmelkov, K., Mairal, J.[Julien], Schmid, C.[Cordelia],
BlitzNet: A Real-Time Deep Network for Scene Understanding,
ICCV17(4174-4182)
IEEE DOI 1802
image segmentation, learning (artificial intelligence), object detection, Semantics BibRef

Zhou, H., Li, Z., Ning, C., Tang, J.,
CAD: Scale Invariant Framework for Real-Time Object Detection,
AutoRob17(760-768)
IEEE DOI 1802
Convolution, Correlation, Detectors, Feature extraction, Object detection, Real-time systems BibRef

Zhang, R.[Rui], Tang, S.[Sheng], Zhang, Y.D.[Yong-Dong], Li, J.T.[Jin-Tao], Yan, S.C.[Shui-Cheng],
Scale-Adaptive Convolutions for Scene Parsing,
ICCV17(2050-2058)
IEEE DOI 1802
Scale adaptive to get both large and small regions. regression analysis, convolutional parameters, convolutional patches, Training BibRef

Xu, H., Dong, M., Zhong, Z.,
Directionally Convolutional Networks for 3D Shape Segmentation,
ICCV17(2717-2726)
IEEE DOI 1802
convolution, image representation, image segmentation, learning (artificial intelligence), shape recognition, BibRef

Chen, X., Zheng, A., Li, J., Lu, F.,
Look, Perceive and Segment: Finding the Salient Objects in Images via Two-stream Fixation-Semantic CNNs,
ICCV17(1050-1058)
IEEE DOI 1802
feature extraction, image segmentation, learning (artificial intelligence), object detection, SOD, Streaming media BibRef

He, S., Jiao, J., Zhang, X., Han, G., Lau, R.W.H.,
Delving into Salient Object Subitizing and Detection,
ICCV17(1059-1067)
IEEE DOI 1802
backpropagation, image representation, neural nets, object detection, adaptive weight layer, Training BibRef

Liu, Y., Li, H., Yan, J., Wei, F., Wang, X., Tang, X.,
Recurrent Scale Approximation for Object Detection in CNN,
ICCV17(571-579)
IEEE DOI 1802
convolution, feature extraction, neural nets, object detection, RSA, convolutional neural network, feature maps, Prediction algorithms BibRef

Moniruzzaman, M.[Mohammed], Islam, S.M.S.[Syed Mohammed Shamsul], Bennamoun, M.[Mohammed], Lavery, P.[Paul],
Deep Learning on Underwater Marine Object Detection: A Survey,
ACIVS17(150-160).
Springer DOI 1712
BibRef

Bianco, S.[Simone], Buzzelli, M.[Marco], Schettini, R.[Raimondo],
A Fully Convolutional Network for Salient Object Detection,
CIAP17(II:82-92).
Springer DOI 1711
BibRef

Li, Q.Q.[Quan-Quan], Jin, S.Y.[Sheng-Ying], Yan, J.J.[Jun-Jie],
Mimicking Very Efficient Network for Object Detection,
CVPR17(7341-7349)
IEEE DOI 1711
Acceleration, Detectors, Feature extraction, Object detection, Proposals, Training BibRef

Kong, X.Y.[Xiang-Yu], Xin, B.[Bo], Wang, Y.Z.[Yi-Zhou], Hua, G.[Gang],
Collaborative Deep Reinforcement Learning for Joint Object Search,
CVPR17(7072-7081)
IEEE DOI 1711
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.],
Straight to Shapes: Real-Time Detection of Encoded Shapes,
CVPR17(4207-4216)
IEEE DOI 1711
Encoding, Object detection, Pipelines, Proposals, Real-time systems, Shape, Training BibRef

Zhang, X.L.[Xiao-Lin], Wei, Y.C.[Yun-Chao], Kang, G.L.[Guo-Liang], Yang, Y.[Yi], Huang, T.S.[Thomas S.],
Self-produced Guidance for Weakly-Supervised Object Localization,
ECCV18(XII: 610-625).
Springer DOI 1810
BibRef

Jie, Z.Q.[Ze-Qun], Wei, Y.C.[Yun-Chao], Jin, X.J.[Xiao-Jie], Feng, J.S.[Jia-Shi], Liu, W.[Wei],
Deep Self-Taught Learning for Weakly Supervised Object Localization,
CVPR17(4294-4302)
IEEE DOI 1711
Correlation, Detectors, Feature extraction, Proposals, Reliability, Support vector machines, Training BibRef

Wu, B., Iandola, F., Jin, P.H., Keutzer, K.,
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving,
ECVW17(446-454)
IEEE DOI 1709
Computational modeling, Detectors, Feature extraction, Object detection, Pipelines, Proposals, Real-time, systems BibRef

Kudo, Y.[Yasunori], Aoki, Y.[Yoshimitsu],
Dilated convolutions for image classification and object localization,
MVA17(452-455)
DOI Link 1708
Computer vision, Error analysis, Image recognition, Image resolution, Image segmentation, Organizations, Pattern recognition BibRef

Roh, M.C., Lee, J.Y.,
Refining faster-RCNN for accurate object detection,
MVA17(514-517)
DOI Link 1708
Art, Detectors, Licenses, Object detection, Organizations, Proposals, Training BibRef

Brahmbhatt, S.[Samarth], Christensen, H.I.[Henrik I.], Hays, J.H.[James H.],
StuffNet: Using 'Stuff' to Improve Object Detection,
WACV17(934-943)
IEEE DOI 1609
Context, Feature extraction, Image segmentation, Object detection, Proposals, Semantics, Training BibRef

Xiang, Y., Choi, W., Lin, Y., Savarese, S.,
Subcategory-Aware Convolutional Neural Networks for Object Proposals and Detection,
WACV17(924-933)
IEEE DOI 1609
Feature extraction, Heating systems, Object detection, Proposals, Training. BibRef

Siam, M., Valipour, S., Jagersand, M., Ray, N.,
Convolutional gated recurrent networks for video segmentation,
ICIP17(3090-3094)
IEEE DOI 1803
BibRef
Earlier: A2, A1, A3, A4:
Recurrent Fully Convolutional Networks for Video Segmentation,
WACV17(29-36)
IEEE DOI 1609
Computer architecture, Deconvolution, Image segmentation, Logic gates, Motion segmentation, Semantics, Training, Video Semantic Segmentation. Neural networks. BibRef

Zhang, J., Dai, Y., Li, B., He, M.,
Attention to the Scale: Deep Multi-Scale Salient Object Detection,
DICTA17(1-7)
IEEE DOI 1804
feature extraction, image fusion, learning (artificial intelligence), neural nets, Semantics BibRef

Zhang, J., Dai, Y., Porikli, F.M.[Fatih M.],
Deep Salient Object Detection by Integrating Multi-level Cues,
WACV17(1-10)
IEEE DOI 1609
Feature extraction, Image resolution, Image segmentation, Machine learning, Neural networks, Object detection, Semantics BibRef

Guo, S.X.[Shu-Xuan], Liu, L.[Li], Wang, W.[Wei], Lao, S.Y.[Song-Yang], Wang, L.[Liang],
An attention model based on spatial transformers for scene recognition,
ICPR16(3757-3762)
IEEE DOI 1705
Databases, Feature extraction, Image recognition, Modeling, Pattern recognition, Pipelines, Visualization BibRef

Toca, C., Patrascu, C., Ciuc, M.,
AutoMarkov DNNs for object classification,
ICPR16(3452-3457)
IEEE DOI 1705
Biological neural networks, Computer architecture, Convolution, Convolutional codes, Markov random fields, Neurons, Testing BibRef

Sun, M., Han, T.X., Liu, M.C.[Ming-Chang], Khodayari-Rostamabad, A.,
Multiple Instance Learning Convolutional Neural Networks for object recognition,
ICPR16(3270-3275)
IEEE DOI 1705
Benchmark testing, Machine learning, Neural networks, Object recognition, Optimization, Prediction algorithms, Training BibRef

Shimoda, W., Yanai, K.,
Weakly-supervised segmentation by combining CNN feature maps and object saliency maps,
ICPR16(1935-1940)
IEEE DOI 1705
Adaptation models, Convolution, Feature extraction, Image segmentation, Proposals, Semantics, Training BibRef

Chen, C.[Chenyi], Liu, M.Y.[Ming-Yu], Tuzel, O.[Oncel], Xiao, J.X.[Jian-Xiong],
R-CNN for Small Object Detection,
ACCV16(V: 214-230).
Springer DOI 1704
BibRef

Wang, Y.[Yida], Cui, C.[Can], Zhou, X.Z.[Xiu-Zhuang], Deng, W.H.[Wei-Hong],
ZigzagNet: Efficient Deep Learning for Real Object Recognition Based on 3D Models,
ACCV16(IV: 456-471).
Springer DOI 1704
BibRef

Nishida, K.[Kenshiro], Hotta, K.[Kazuhiro],
Particle Detection in Crowd Regions Using Cumulative Score of CNN,
ISVC16(II: 566-575).
Springer DOI 1701
BibRef

Wang, C., Siddiqi, K.,
Differential Geometry Boosts Convolutional Neural Networks for Object Detection,
DIFF-CV16(1006-1013)
IEEE DOI 1612
BibRef

Tran, D., Wang, H., Feiszli, M., Torresani, L.,
Video Classification With Channel-Separated Convolutional Networks,
ICCV19(5551-5560)
IEEE DOI 2004
convolutional neural nets, image classification, learning (artificial intelligence), neural net architecture, Computational efficiency BibRef

Tran, D.[Du], Bourdev, L.[Lubomir], Fergus, R.[Rob], Torresani, L.[Lorenzo], Paluri, M.[Manohar],
Deep End2End Voxel2Voxel Prediction,
DeepLearn-C16(402-409)
IEEE DOI 1612
BibRef

Yang, B.[Bin], Yan, J.J.[Jun-Jie], Lei, Z.[Zhen], Li, S.Z.[Stan Z.],
CRAFT Objects from Images,
CVPR16(6043-6051)
IEEE DOI 1612
CRAFT: Cascade Regionproposal-network And FasT-rcnn. BibRef

Li, C.Y.[Chun-Yuan], Stevens, A.[Andrew], Chen, C.Y.[Chang-You], Pu, Y.C.[Yun-Chen], Gan, Z.[Zhe], Carin, L.[Lawrence],
Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification,
CVPR16(5666-5675)
IEEE DOI 1612
2d, 3D shape cues. BibRef

Qi, C.R.[Charles R.], Su, H.[Hao], Nießner, M.[Matthias], Dai, A.[Angela], Yan, M.Y.[Meng-Yuan], Guibas, L.J.[Leonidas J.],
Volumetric and Multi-view CNNs for Object Classification on 3D Data,
CVPR16(5648-5656)
IEEE DOI 1612
BibRef

Kuen, J.[Jason], Wang, Z.H.[Zhen-Hua], Wang, G.[Gang],
Recurrent Attentional Networks for Saliency Detection,
CVPR16(3668-3677)
IEEE DOI 1612
BibRef

Mathe, S., Pirinen, A., Sminchisescu, C.[Cristian],
Reinforcement Learning for Visual Object Detection,
CVPR16(2894-2902)
IEEE DOI 1612
BibRef

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
Computer architecture, 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

Tang, Y.[Youbao], Wu, X.Q.[Xiang-Qian],
Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs,
ECCV16(VIII: 809-825).
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 .