14.5.7.5.4 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

Li, X., Zhao, L., Wei, L., Yang, M.H., Wu, F., Zhuang, Y., Ling, H., Wang, J.,
DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection,
IP(25), No. 8, August 2016, pp. 3919-3930.
IEEE DOI 1608
Computational modeling BibRef

Zhang, X.Y.[Xiang-Yu], Zou, J.H.[Jian-Hua], He, K.M.[Kai-Ming], Sun, J.[Jian],
Accelerating Very Deep Convolutional Networks for Classification and Detection,
PAMI(38), No. 10, October 2016, pp. 1943-1955.
IEEE DOI 1609
Acceleration BibRef

He, Y., Zhang, X.Y.[Xiang-Yu], Sun, J.[Jian],
Channel Pruning for Accelerating Very Deep Neural Networks,
ICCV17(1398-1406)
IEEE DOI 1802
iterative methods, learning (artificial intelligence), least squares approximations, neural nets, regression analysis, Training BibRef

Zhang, X.Y.[Xiang-Yu], Zou, J.H.[Jian-Hua], Ming, X.[Xiang], He, K.M.[Kai-Ming], Sun, J.[Jian],
Efficient and accurate approximations of nonlinear convolutional networks,
CVPR15(1984-1992)
IEEE DOI 1510
BibRef

He, K.M.[Kai-Ming], Zhang, X.Y.[Xiang-Yu], Ren, S.Q.[Shao-Qing], Sun, J.[Jian],
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification,
ICCV15(1026-1034)
IEEE DOI 1602
Adaptation models BibRef

He, K.M.[Kai-Ming], Zhang, X.Y.[Xiang-Yu], Ren, S.Q.[Shao-Qing], Sun, J.[Jian],
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition,
PAMI(37), No. 9, September 2015, pp. 1904-1916.
IEEE DOI 1508
BibRef
Earlier: ECCV14(III: 346-361).
Springer DOI 1408
Accuracy BibRef

He, K.M.[Kai-Ming], Zhang, X.Y.[Xiang-Yu], Ren, S.Q.[Shao-Qing], Sun, J.[Jian],
Deep Residual Learning for Image Recognition,
CVPR16(770-778)
IEEE DOI 1612
Award, CVPR. BibRef
And:
Identity Mappings in Deep Residual Networks,
ECCV16(IV: 630-645).
Springer DOI 1611
BibRef

Ren, S.Q.[Shao-Qing], He, K.M.[Kai-Ming], Girshick, R.[Ross], Sun, J.[Jian],
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,
PAMI(39), No. 6, June 2017, pp. 1137-1149.
IEEE DOI 1705
Convolutional codes, Detectors, Feature extraction, Object detection, Proposals, Search problems, Training, convolutional neural network, region proposal. 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., Zhang, D., Cheng, G., Liu, N., Xu, D.,
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

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

Ren, Y.[Yun], Zhu, C.[Changren], Xiao, S.[Shunping],
Deformable Faster R-CNN with Aggregating Multi-Layer Features for Partially Occluded Object Detection in Optical Remote Sensing Images,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
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., Zhao, C., Guo, H., Wang, J., Zhao, X., Lu, H.,
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

Guan, W.L.[Wen-Long], Wang, T.T.[Tian-Tian], Qi, J.Q.[Jin-Qing], Zhang, L.H.[Li-He], Lu, H.C.[Hu-Chuan],
Edge-Aware Convolution Neural Network Based Salient Object Detection,
SPLetters(26), No. 1, January 2019, pp. 114-118.
IEEE DOI 1901
edge detection, feature extraction, feedforward neural nets, learning (artificial intelligence), object detection, convolutional neural networks (CNNs) BibRef

Zhang, P.P.[Ping-Ping], Liu, W.[Wei], Wang, H.Y.[Hong-Yu], Lei, Y.J.[Yin-Jie], Lu, H.C.[Hu-Chuan],
Deep gated attention networks for large-scale street-level scene segmentation,
PR(88), 2019, pp. 702-714.
Elsevier DOI 1901
Scene segmentation, Fully convolutional network, Spatial gated attention, Street-level image understanding BibRef

Yan, J.[Jiangqiao], Wang, H.[Hongqi], Yan, M.L.[Meng-Long], Diao, W.H.[Wen-Hui], Sun, X.[Xian], Li, H.[Hao],
IoU-Adaptive Deformable R-CNN: Make Full Use of IoU for Multi-Class Object Detection in Remote Sensing Imagery,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902
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

Hou, Q., Cheng, M.M., Hu, X., Borji, A., Tu, Z., Torr, P.H.S.[Philip H.S.],
Deeply Supervised Salient Object Detection with Short Connections,
PAMI(41), No. 4, April 2019, pp. 815-828.
IEEE DOI 1903
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.[Jihua], Wang, J.[Jiaxing], Pang, S.[Shanmin], Guan, W.[Weili], Li, Z.[Zhongyu], Li, Y.[Yaochen], 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

Cao, Y.P.[Yan-Peng], Fu, G.Z.[Gui-Zhong], Yang, J.X.[Jiang-Xin], Cao, Y.L.[Yan-Long], Yang, M.Y.[Michael Ying],
Accurate salient object detection via dense recurrent connections and residual-based hierarchical feature integration,
SP:IC(78), 2019, pp. 103-112.
Elsevier DOI 1909
Convolutional neural network, Recurrent convolutional layer, Salient object detection, Hierarchical feature fusion, Deep supervision 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

Liu, Y., Han, J., Zhang, Q., Shan, C.,
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

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

Ghosh, S., Srinivasa, S.K.K., Amon, P., Hutter, A., Kaup, A.,
Deep Network Pruning for Object Detection,
ICIP19(3915-3919)
IEEE DOI 1910
Object Detection, Deep Learning, CNN, Network Pruning, Clustering BibRef

Zhang, C., Cao, Z., Xiong, X., Xian, K., Qi, X.,
Salient Object Detection via Deep Hierarchical Context Aggregation and Multi-Layer Supervision,
ICIP19(2941-2945)
IEEE DOI 1910
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,
ICIP19(61-65)
IEEE DOI 1910
Monocular 3D object detection, convolutional neural networks, autonomous driving, geometric constraints BibRef

Sharma, R.[Raghav], Pandey, R.[Rohit], Nigam, A.[Aditya],
Real Time Object Detection on Aerial Imagery,
CAIP19(I:481-491).
Springer DOI 1909
Low object to image ratio. Lots of small objects in a very large image. BibRef

Wu, W.[Wenbo], Payeur, P.[Pierre], Al-Buraiki, O.[Omar], Ross, M.[Matthew],
Vision-Based Target Objects Recognition and Segmentation for Unmanned Systems Task Allocation,
ICIAR19(I:252-263).
Springer DOI 1909
BibRef

Soviany, P.[Petru], Ionescu, R.T.[Radu Tudor],
Frustratingly Easy Trade-off Optimization Between Single-Stage and Two-Stage Deep Object Detectors,
CEFR-LCV18(IV:366-378).
Springer DOI 1905
BibRef

Lahiri, A.[Avisek], Ragireddy, S.C.[Sri Charan], Biswas, P.[Prabir], Mitra, P.[Pabitra],
Unsupervised Adversarial Visual Level Domain Adaptation for Learning Video Object Detectors From Images,
WACV19(1807-1815)
IEEE DOI 1904
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.,
Ventral-Dorsal Neural Networks: Object Detection Via Selective Attention,
WACV19(986-994)
IEEE DOI 1904
brain, convolutional neural nets, feature extraction, image classification, neurophysiology, object detection, Training 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

Chen, Y., Li, W., Sakaridis, C., Dai, D., Van Gool, L.J.,
Domain Adaptive Faster R-CNN for Object Detection in the Wild,
CVPR18(3339-3348)
IEEE DOI 1812
Object detection, Training, Adaptation models, Proposals, Task analysis, Lighting, Feature extraction BibRef

Tang, M., Djelouah, A., Perazzi, F., Boykov, Y., Schroers, C.,
Normalized Cut Loss for Weakly-Supervised CNN Segmentation,
CVPR18(1818-1827)
IEEE DOI 1812
Image segmentation, Training, Proposals, Standards, Semisupervised learning, Entropy, Semantics BibRef

Zhang, Y., Guo, Y., Jin, Y., Luo, Y., He, Z., Lee, H.,
Unsupervised Discovery of Object Landmarks as Structural Representations,
CVPR18(2694-2703)
IEEE DOI 1812
Visualization, Neural networks, Decoding, Image reconstruction, Training, Detectors BibRef

Uijlings, J.R.R., Popov, S., Ferrari, V.,
Revisiting Knowledge Transfer for Training Object Class Detectors,
CVPR18(1101-1110)
IEEE DOI 1812
Proposals, Detectors, Training, Knowledge transfer, Semantics, Neural networks, Standards BibRef

Hua, B., Tran, M., Yeung, S.,
Pointwise Convolutional Neural Networks,
CVPR18(984-993)
IEEE DOI 1812
Convolution, Semantics, Kernel, Shape, Object recognition, Task analysis BibRef

Li, Y., Zhang, X., Chen, D.,
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes,
CVPR18(1091-1100)
IEEE DOI 1812
Feature extraction, Convolution, Kernel, Task analysis, Training, Image analysis, Pattern recognition BibRef

Cai, Z., Vasconcelos, N.,
Cascade R-CNN: Delving Into High Quality Object Detection,
CVPR18(6154-6162)
IEEE DOI 1812
Detectors, Object detection, Proposals, Training, Computer architecture, Task analysis, Noise measurement BibRef

Morris, D.,
A Pyramid CNN for Dense-Leaves Segmentation,
CRV18(238-245)
IEEE DOI 1812
Image segmentation, Task analysis, Image edge detection, Object segmentation, Shape, Training, Boundary detection BibRef

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,
CRV18(95-101)
IEEE DOI 1812
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,
CRV18(182-189)
IEEE DOI 1812
Robots, Video recording, Trajectory, Detectors, Lighting, Semantics, Feature extraction, visual teach and repeat, semantic navigation, CNN based object detector BibRef

Wang, P.[Peng], Yuille, A.L.[Alan L.],
DOC: Deep OCclusion Estimation from a Single Image,
ECCV16(I: 545-561).
Springer DOI 1611
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Tang, Z.Q.[Zhi-Qiang], Peng, X.[Xi], Geng, S.J.[Shi-Jie], Wu, L.F.[Ling-Fei], Zhang, S.T.[Shao-Ting], Metaxas, D.[Dimitris],
Quantized Densely Connected U-Nets for Efficient Landmark Localization,
ECCV18(III: 348-364).
Springer DOI 1810
BibRef

Wu, Z.X.[Zu-Xuan], Han, X.T.[Xin-Tong], Lin, Y.L.[Yen-Liang], Uzunbas, M.G.[Mustafa Gökhan], Goldstein, T.[Tom], Lim, S.N.[Ser Nam], Davis, L.S.[Larry S.],
DCAN: Dual Channel-Wise Alignment Networks for Unsupervised Scene Adaptation,
ECCV18(VI: 535-552).
Springer DOI 1810
pixel annotations to train NN for segmentation. BibRef

Wei, Y.[Yi], Pan, X.Y.[Xin-Yu], Qin, H.W.[Hong-Wei], Ouyang, W.L.[Wan-Li], Yan, J.J.[Jun-Jie],
Quantization Mimic: Towards Very Tiny CNN for Object Detection,
ECCV18(VIII: 274-290).
Springer DOI 1810
BibRef

Liu, S.T.[Song-Tao], Huang, D.[Di], Wang, Y.H.[Yun-Hong],
Receptive Field Block Net for Accurate and Fast Object Detection,
ECCV18(XI: 404-419).
Springer DOI 1810
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Gu, J.Y.[Jia-Yuan], Hu, H.[Han], Wang, L.[Liwei], Wei, Y.[Yichen], Dai, J.[Jifeng],
Learning Region Features for Object Detection,
ECCV18(XII: 392-406).
Springer DOI 1810
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Kim, Y.H.[Yong-Hyun], Kang, B.N.[Bong-Nam], Kim, D.J.[Dai-Jin],
SAN: Learning Relationship Between Convolutional Features for Multi-scale Object Detection,
ECCV18(VI: 328-343).
Springer DOI 1810
BibRef

Choi, H., Bajic, I.V.,
Deep Feature Compression for Collaborative Object Detection,
ICIP18(3743-3747)
IEEE DOI 1809
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)
IEEE DOI 1809
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)
IEEE DOI 1809
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

Ouadiay, F.Z., Bouftaih, H., Bouyakhf, E.H., Himmi, M.M.,
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

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

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],
A MultiPath Network for Object Detection,
BMVC16(xx-yy).
HTML Version. 1805
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Tripathi, S.[Subarna], Lipton, Z.[Zachary], Belongie, S.[Serge], Nguyen, T.[Truong],
Context Matters: Refining Object Detection in Video with Recurrent Neural Networks,
BMVC16(xx-yy).
HTML Version. 1805
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Lou, Y., Fu, G., Jiang, Z., Men, A., Zhou, Y.,
Improve object detection via a multi-feature and multi-task CNN model,
VCIP17(1-4)
IEEE DOI 1804
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)
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

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],
DSOD: Learning Deeply Supervised Object Detectors from Scratch,
ICCV17(1937-1945)
IEEE DOI 1802
image classification, learning (artificial intelligence), object detection, DSOD, Training data 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

He, K., Gkioxari, G., Dollár, P., Girshick, R.,
Mask R-CNN,
ICCV17(2980-2988)
IEEE DOI 1802
feature extraction, image segmentation, object detection, pose estimation, Faster R-CNN, bounding-box object detection, Semantics 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
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Bianco, S.[Simone], Buzzelli, M.[Marco], Schettini, R.[Raimondo],
A Fully Convolutional Network for Salient Object Detection,
CIAP17(II:82-92).
Springer DOI 1711
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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
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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
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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
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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
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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
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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
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Kuen, J.[Jason], Wang, Z.H.[Zhen-Hua], Wang, G.[Gang],
Recurrent Attentional Networks for Saliency Detection,
CVPR16(3668-3677)
IEEE DOI 1612
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Mathe, S., Pirinen, A., Sminchisescu, C.[Cristian],
Reinforcement Learning for Visual Object Detection,
CVPR16(2894-2902)
IEEE DOI 1612
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Misra, I.[Ishan], Zitnick, C.L.[C. Lawrence], Mitchell, M.[Margaret], Girshick, R.[Ross],
Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels,
CVPR16(2930-2939)
IEEE DOI 1612
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Bell, S.[Sean], Zitnick, C.L.[C. Lawrence], Bala, K.[Kavita], Girshick, R.[Ross],
Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks,
CVPR16(2874-2883)
IEEE DOI 1612
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Najibi, M., Rastegari, M., Davis, L.S.,
G-CNN: An Iterative Grid Based Object Detector,
CVPR16(2369-2377)
IEEE DOI 1612
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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
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Yang, F., Choi, W., Lin, Y.,
Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers,
CVPR16(2129-2137)
IEEE DOI 1612
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Hayder, Z., He, X., Salzmann, M.,
Learning to Co-Generate Object Proposals with a Deep Structured Network,
CVPR16(2565-2573)
IEEE DOI 1612
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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
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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

Mopuri, K.R.[Konda Reddy], Babu, R.V.[R. Venkatesh],
Object level deep feature pooling for compact image representation,
DeepLearn15(62-70)
IEEE DOI 1510
Computational modeling 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
Neural Networks Applied to Specific Problems .