14.5.8.6.7 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
BibRef

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

Wang, H.Z.[Hong-Zhen], Wang, Y.[Ying], Zhang, Q.[Qian], Xiang, S.M.[Shi-Ming], Pan, C.H.[Chun-Hong],
Gated Convolutional Neural Network for Semantic Segmentation in High-Resolution Images,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Guo, S.C.[Shi-Chen], Jin, Q.Z.[Qi-Zhao], Wang, H.Z.[Hong-Zhen], Wang, X.Z.[Xue-Zhi], Wang, Y.G.[Yan-Gang], Xiang, S.M.[Shi-Ming],
Learnable Gated Convolutional Neural Network for Semantic Segmentation in Remote-Sensing Images,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Afridi, M.J.[Muhammad Jamal], Ross, A.[Arun], Shapiro, E.M.[Erik M.],
On automated source selection for transfer learning in convolutional neural networks,
PR(73), No. 1, 2018, pp. 65-75.
Elsevier DOI 1709
Transfer learning BibRef

Xu, N.[Nuo], Huo, C.L.[Chun-Lei],
Learning Deep Relationship for Object Detection,
IEICE(E101-D), No. 1, January 2018, pp. 273-276.
WWW Link. 1801
BibRef

Han, J.W.[Jun-Wei], Zhang, D.W.[Ding-Wen], Cheng, G.[Gong], Liu, N.[Nian], Xu, D.[Dong],
Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey,
SPMag(35), No. 1, January 2018, pp. 84-100.
IEEE DOI 1801
Survey, Deep Nets. Computer architecture, Computer vision, Convolution, Feature extraction, Machine learning, Object detection, Visualization BibRef

Liu, Y.[Yi], Han, J.G.[Jun-Gong], Zhang, Q.[Qiang], Shan, C.F.[Cai-Feng],
Deep Salient Object Detection With Contextual Information Guidance,
IP(29), No. 1, 2020, pp. 360-374.
IEEE DOI 1910
convolutional neural nets, learning (artificial intelligence), object detection, deep salient object detection, multi-level contextual information integration BibRef

Liu, Y.[Yi], Zhang, Q.[Qiang], Zhang, D.W.[Ding-Wen], Han, J.G.[Jun-Gong],
Employing Deep Part-Object Relationships for Salient Object Detection,
ICCV19(1232-1241)
IEEE DOI 2004
convolutional neural nets, learning (artificial intelligence), object detection, convolutional neural networks, Noise measurement BibRef

Xu, Z.Z.[Zhao-Zhuo], Xu, X.[Xin], Wang, L.[Lei], Yang, R.[Rui], Pu, F.L.[Fang-Ling],
Deformable ConvNet with Aspect Ratio Constrained NMS for Object Detection in Remote Sensing Imagery,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
BibRef

Guo, W.[Wei], Yang, W.[Wen], Zhang, H.J.[Hai-Jian], Hua, G.[Guang],
Geospatial Object Detection in High Resolution Satellite Images Based on Multi-Scale Convolutional Neural Network,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
BibRef

Maninis, K.K.[Kevis-Kokitsi], Pont-Tuset, J.[Jordi], Arbeláez, P.[Pablo], Van Gool, L.J.[Luc J.],
Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks,
PAMI(40), No. 4, April 2018, pp. 819-833.
IEEE DOI 1804
BibRef
Earlier:
Convolutional Oriented Boundaries,
ECCV16(I: 580-596).
Springer DOI 1611
computer vision, image classification, image representation, image segmentation, neural nets, object detection, COB, semantic contours BibRef

Shi, W.W.[Wei-Wei], Gong, Y.H.[Yi-Hong], Cheng, D.[De], Tao, X.Y.[Xiao-Yu], Zheng, N.N.[Nan-Ning],
Entropy and orthogonality based deep discriminative feature learning for object recognition,
PR(81), 2018, pp. 71-80.
Elsevier DOI 1806
Convolutional neural network (CNN), Discriminative feature learning, Entropy, Orthogonality, Object recognition BibRef

Bi, L.[Lei], Feng, D.[Dagan], Kim, J.M.[Jin-Man],
Dual-Path Adversarial Learning for Fully Convolutional Network (FCN)-Based Medical Image Segmentation,
VC(34), No. 6-8, June 2018, pp. 1043-1052.
WWW Link. 1806
BibRef

Ding, P.[Peng], Zhang, Y.[Ye], Deng, W.J.[Wei-Jian], Jia, P.[Ping], Kuijper, A.[Arjan],
A Light and Faster Regional Convolutional Neural Network for Object Detection in Optical Remote Sensing Images,
PandRS(141), 2018, pp. 208-218.
Elsevier DOI 1806
Deep convolution neural network, Deep learning (DL), Remote sensing images, Object detection BibRef

Zeng, X.Y.[Xing-Yu], Ouyang, W.L.[Wan-Li], Yan, J.J.[Jun-Jie], Li, H.S.[Hong-Sheng], Xiao, T.[Tong], Wang, K.[Kun], Liu, Y.[Yu], Zhou, Y.C.[Yu-Cong], Yang, B.[Bin], Wang, Z.[Zhe], Zhou, H.[Hui], Wang, X.G.[Xiao-Gang],
Crafting GBD-Net for Object Detection,
PAMI(40), No. 9, September 2018, pp. 2109-2123.
IEEE DOI 1808
gated bi-directional CNN. Object detection, Rabbits, Visualization, Feature extraction, Head, Proposals, Logic gates, Convolutional neural network, CNN, object detection 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
BibRef

Liang, X.D.[Xiao-Dan], Lin, L.[Liang], Wei, Y.C.[Yun-Chao], Shen, X.H.[Xiao-Hui], Yang, J.C.[Jian-Chao], Yan, S.C.[Shui-Cheng],
Proposal-Free Network for Instance-Level Object Segmentation,
PAMI(40), No. 12, December 2018, pp. 2978-2991.
IEEE DOI 1811
Convolutional neural networks, Object segmentation, Semantics, Image segmentation, Object detection, Neural networks, convolutional neural network 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

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

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

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

Wang, W.G.[Wen-Guan], Zhao, S.Y.[Shu-Yang], Shen, J.B.[Jian-Bing], Hoi, S.C.H.[Steven C. H.], Borji, A.[Ali],
Deeply Supervised Salient Object Detection with Short Connections,
PAMI(41), No. 4, April 2019, pp. 815-828.
IEEE DOI 1903
BibRef
And:
Salient Object Detection With Pyramid Attention and Salient Edges,
CVPR19(1448-1457).
IEEE DOI 2002
BibRef
Earlier: CVPR17(5300-5309)
IEEE DOI 1711
Object detection, Feature extraction, Image edge detection, Image segmentation, Semantics, Saliency detection, edge detection. Computer architecture, Image edge detection, Neural networks. BibRef

Zhu, J.[Jun], Zhu, J.[Jiangcheng], 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
BibRef

Zhu, J.H.[Ji-Hua], Wang, J.X.[Jia-Xing], Pang, S.M.[Shan-Min], Guan, W.[Weili], Li, Z.Y.[Zhong-Yu], Li, Y.C.[Yao-Chen], Qian, X.M.[Xue-Ming],
Co-weighting semantic convolutional features for object retrieval,
JVCIR(62), 2019, pp. 368-380.
Elsevier DOI 1908
Object retrieval, Deep convolutional features, Aggregation BibRef

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

Ghassemi, S.[Sina], Fiandrotti, A.[Attilio], Francini, G.[Gianluca], Magli, E.[Enrico],
Learning and Adapting Robust Features for Satellite Image Segmentation on Heterogeneous Data Sets,
GeoRS(57), No. 9, September 2019, pp. 6517-6529.
IEEE DOI 1909
Image segmentation, Satellites, Training, Semantics, Feature extraction, Labeling, Computer architecture, satellite image segmentation 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
BibRef

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

Zhang, D.W.[Ding-Wen], Han, J.W.[Jun-Wei], Guo, G.Y.[Guang-Yu], Zhao, L.[Long],
Learning Object Detectors With Semi-Annotated Weak Labels,
CirSysVideo(29), No. 12, December 2019, pp. 3622-3635.
IEEE DOI 1912
Training, Object detection, Detectors, Training data, Generators, Visualization, Semantics, Computer vision, image processing, learning (artificial intelligence) BibRef

Li, K.[Ke], Wan, G.[Gang], Cheng, G.[Gong], Meng, L.Q.[Li-Qiu], Han, J.W.[Jun-Wei],
Object detection in optical remote sensing images: A survey and a new benchmark,
PandRS(159), 2020, pp. 296-307.
Elsevier DOI 1912
Object detection, Deep learning, Convolutional Neural Network (CNN), Benchmark dataset, Optical remote sensing images 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
BibRef

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
BibRef

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
BibRef

Zhang, R.M.[Rui-Mao], Lin, L.[Liang], Wang, G.R.[Guang-Run], Wang, M.[Meng], Zuo, W.M.[Wang-Meng],
Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions,
PAMI(41), No. 3, March 2019, pp. 596-610.
IEEE DOI 1902
Semantics, Labeling, Training, Neural networks, Task analysis, Predictive models, Image segmentation, Scene parsing, recursive structured prediction BibRef

Lin, L.[Liang], Wang, G.R.[Guang-Run], Zhang, R.[Rui], Zhang, R.M.[Rui-Mao], Liang, X.D.[Xiao-Dan], Zuo, W.M.[Wang-Meng],
Deep Structured Scene Parsing by Learning with Image Descriptions,
CVPR16(2276-2284)
IEEE DOI 1612
BibRef

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

Lai, H.J.[Han-Jiang], Chen, J.K.[Ji-Kai], Geng, L.B.[Li-Bing], Pan, Y.[Yan], Liang, X.D.[Xiao-Dan], Yin, J.[Jian],
Improving Deep Binary Embedding Networks by Order-Aware Reweighting of Triplets,
CirSysVideo(30), No. 4, April 2020, pp. 1162-1172.
IEEE DOI 2004
Binary codes, Training, Hash functions, Image retrieval, Semantics, Quantization (signal), Dogs, Image retrieval, triplet ranking loss, nearest neighbor search 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

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
BibRef

Wu, M.H.[Ming-Hu], Yue, H.H.[Han-Hui], Wang, J.[Juan], Huang, Y.[Yongxi], 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
BibRef

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

Xu, Y., Dai, W., Qi, Y., Zou, J., Xiong, H.,
Iterative Deep Neural Network Quantization With Lipschitz Constraint,
MultMed(22), No. 7, July 2020, pp. 1874-1888.
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Quantization (signal), Neural networks, Convolution, Computational modeling, Semantics, Object detection, Image coding, Lipschitz constraint BibRef

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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,
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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,
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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,
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IEEE DOI 2009
Object detection, Detectors, Training, Heating systems, Feature extraction, Visualization, Convolutional neural networks, convolutional neural networks BibRef

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, Computer architecture, 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, Computer architecture, 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., Sun, Y., 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

Farhadi, M.[Mohammad], Yang, Y.Z.[Ye-Zhou],
TKD: Temporal Knowledge Distillation for Active Perception,
WACV20(942-951)
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Code, Object Detection.
WWW Link. Temporal knowledge over NN applied over multiple frames. Adaptation models, Object detection, Visualization, Computational modeling, Task analysis, Training, Feature extraction 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)
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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
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)
IEEE DOI 2004
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)
IEEE DOI 2004
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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Code, Object Detection.
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Hedayati, H., McGuinness, B.J., Cree, M.J., Perrone, J.A.,
Generalization Approach for CNN-based Object Detection in Unconstrained Outdoor Environments,
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

Ghosh, S., Srinivasa, S.K.K., Amon, P., Hutter, A., Kaup, A.,
Deep Network Pruning for Object Detection,
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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ICIP19(2941-2945)
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saliency detection, deep layer aggregation, intermediate supervision BibRef

Naiden, A., Paunescu, V., Kim, G., Jeon, B., Leordeanu, M.,
Shift R-CNN: Deep Monocular 3D Object Detection With Closed-Form Geometric Constraints,
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image annotation, object detection, unsupervised learning, video signal processing, unannotated video dataset, Image color analysis BibRef

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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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Hua, B., Tran, M., Yeung, S.,
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Li, Y., Zhang, X., Chen, D.,
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Cai, Z., Vasconcelos, N.,
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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

Toudeshki, A.G., Shamshirdar, F., Vaughan, R.,
Robust UAV Visual Teach and Repeat Using Only Sparse Semantic Object Features,
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Deep Feature Compression for Collaborative Object Detection,
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Quantization (signal), Image coding, Cloud computing, Training, Tensile stress, Collaboration, Object detection, object detection BibRef

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

Zhuang, Y., Tao, L., Yang, F., Ma, C., Zhang, Z., Jia, H., Xie, X.,
RelationNet: Learning Deep-Aligned Representation for Semantic Image Segmentation,
ICPR18(1506-1511)
IEEE DOI 1812
Feature extraction, Convolution, Image segmentation, Training, Estimation, Semantics, Correlation BibRef

Zhuang, Y., Yang, F., Tao, L., Ma, C., Zhang, Z., Li, Y., Jia, H., Xie, X., Gao, W.,
Dense Relation Network: Learning Consistent and Context-Aware Representation for Semantic Image Segmentation,
ICIP18(3698-3702)
IEEE DOI 1809
Feature extraction, Semantics, Image segmentation, Training, Recurrent neural networks, Aggregates, Benchmark testing, Context-Restricted Loss 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

Yamashita, T., Furukawa, H., Fujiyoshi, H.,
Multiple Skip Connections of Dilated Convolution Network for Semantic Segmentation,
ICIP18(1593-1597)
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Convolution, Decoding, Semantics, Image segmentation, Task analysis, Deconvolution, Cameras, deep learning, semantic segmentation 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

Amin, S.[Sikandar], Galasso, F.[Fabio],
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)
IEEE DOI 1806
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)
IEEE DOI 1806
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)
IEEE DOI 1806
Gaussian distribution, convolution, feedforward neural nets, learning (artificial intelligence), object detection, Visualization BibRef

Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., Cottrell, G.,
Understanding Convolution for Semantic Segmentation,
WACV18(1451-1460)
IEEE DOI 1806
convolution, feedforward neural nets, image coding, image resolution, image segmentation, Training BibRef

Ye, L., Liu, Z., Wang, Y.,
Learning Semantic Segmentation with Diverse Supervision,
WACV18(1461-1469)
IEEE DOI 1806
computer vision, feedforward neural nets, image classification, image segmentation, learning (artificial intelligence), Training 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

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Improve object detection via a multi-feature and multi-task CNN model,
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],
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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)
IEEE DOI 1804
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, E.[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

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

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 .