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.,
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
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
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IEICE(E103-D), No. 3, March 2020, pp. 648-659.
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2003
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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
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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
Li, Y.,
Chen, Y.,
Wang, N.,
Zhang, Z.,
Scale-Aware Trident Networks for Object Detection,
ICCV19(6053-6062)
IEEE DOI
2004
Code, Object Detection.
WWW Link. feature extraction,
learning (artificial intelligence), neural nets,
Computer architecture
BibRef
Li, P.,
Chen, B.,
Ouyang, W.,
Wang, D.,
Yang, X.,
Lu, H.,
GradNet: Gradient-Guided Network for Visual Object Tracking,
ICCV19(6161-6170)
IEEE DOI
2004
convolutional neural nets, feature extraction, gradient methods,
object detection, object tracking, visual object tracking,
Real-time systems
BibRef
Xu, H.,
Yao, L.,
Li, Z.,
Liang, X.,
Zhang, W.,
Auto-FPN: Automatic Network Architecture Adaptation for Object
Detection Beyond Classification,
ICCV19(6648-6657)
IEEE DOI
2004
gradient methods, image classification,
object detection, NAS, CNN architecture, image classification, Training
BibRef
Yang, K.,
Li, D.,
Dou, Y.,
Towards Precise End-to-End Weakly Supervised Object Detection Network,
ICCV19(8371-8380)
IEEE DOI
2004
learning (artificial intelligence), object detection,
regression analysis, object position prediction, local minima,
BibRef
Peng, J.R.[Jun-Ran],
Sun, M.[Ming],
Zhang, Z.X.[Zhao-Xiang],
Tan, T.N.[Tie-Niu],
Yan, J.J.[Jun-Jie],
POD: Practical Object Detection With Scale-Sensitive Network,
ICCV19(9606-9615)
IEEE DOI
2004
image filtering, learning (artificial intelligence),
object detection, optimisation, practical object detection,
Optimization
BibRef
Tian, Z.,
Shen, C.,
Chen, H.,
He, T.,
FCOS: Fully Convolutional One-Stage Object Detection,
ICCV19(9626-9635)
IEEE DOI
2004
Code, Object Detection.
WWW Link. image segmentation, object detection, predefined anchor boxes,
final detection performance, pre-defined set, anchor box free,
Head
BibRef
Nie, J.,
Anwer, R.M.,
Cholakkal, H.,
Khan, F.S.,
Pang, Y.,
Shao, L.,
Enriched Feature Guided Refinement Network for Object Detection,
ICCV19(9536-9545)
IEEE DOI
2004
Code, Object Detection.
WWW Link. feature extraction, image classification,
learning (artificial intelligence), neural nets,
Benchmark testing
BibRef
Aghdam, H.H.,
Gonzalez-Garcia, A.,
Lopez, A.,
Weijer, J.,
Active Learning for Deep Detection Neural Networks,
ICCV19(3671-3679)
IEEE DOI
2004
learning (artificial intelligence), neural nets,
object detection, pedestrians, active learning,
BibRef
Zeng, Z.,
Liu, B.,
Fu, J.,
Chao, H.,
Zhang, L.,
WSOD2: Learning Bottom-Up and Top-Down Objectness Distillation for
Weakly-Supervised Object Detection,
ICCV19(8291-8299)
IEEE DOI
2004
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)
IEEE DOI
2004
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.,
Learning Rich Features at High-Speed for Single-Shot Object Detection,
ICCV19(1971-1980)
IEEE DOI
2004
Code, Object Detection.
WWW Link. image classification, image representation,
learning (artificial intelligence), object detection, Training
BibRef
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)
IEEE DOI
2004
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)
IEEE DOI
2004
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)
IEEE DOI
2004
data mining, image classification, image representation,
image segmentation, learning (artificial intelligence), Standards
BibRef
Dusmanu, M.[Mihai],
Rocco, I.[Ignacio],
Pajdla, T.[Tomas],
Pollefeys, M.[Marc],
Sivic, J.[Josef],
Torii, A.[Akihiko],
Sattler, T.[Torsten],
D2-Net: A Trainable CNN for Joint Description and Detection of Local
Features,
CVPR19(8084-8093).
IEEE DOI
2002
BibRef
Zhu, R.[Rui],
Zhang, S.F.[Shi-Feng],
Wang, X.B.[Xiao-Bo],
Wen, L.Y.[Long-Yin],
Shi, H.L.[Hai-Lin],
Bo, L.F.[Lie-Feng],
Mei, T.[Tao],
ScratchDet: Training Single-Shot Object Detectors From Scratch,
CVPR19(2263-2272).
IEEE DOI
2002
BibRef
Pang, Y.W.[Yan-Wei],
Wang, T.[Tiancai],
Anwer, R.M.[Rao Muhammad],
Khan, F.S.[Fahad Shahbaz],
Shao, L.[Ling],
Efficient Featurized Image Pyramid Network for Single Shot Detector,
CVPR19(7328-7336).
IEEE DOI
2002
BibRef
Pang, J.M.[Jiang-Miao],
Chen, K.[Kai],
Shi, J.P.[Jian-Ping],
Feng, H.J.[Hua-Jun],
Ouyang, W.L.[Wan-Li],
Lin, D.[Dahua],
Libra R-CNN: Towards Balanced Learning for Object Detection,
CVPR19(821-830).
IEEE DOI
2002
BibRef
Voigtlaender, P.[Paul],
Chai, Y.N.[Yu-Ning],
Schroff, F.[Florian],
Adam, H.[Hartwig],
Leibe, B.[Bastian],
Chen, L.C.[Liang-Chieh],
FEELVOS: Fast End-To-End Embedding Learning for Video Object
Segmentation,
CVPR19(9473-9482).
IEEE DOI
2002
BibRef
Zhang, C.[Chen],
Kim, J.[Joohee],
Object Detection With Location-Aware Deformable Convolution and
Backward Attention Filtering,
CVPR19(9444-9453).
IEEE DOI
2002
BibRef
Derakhshani, M.M.[Mohammad Mahdi],
Masoudnia, S.[Saeed],
Shaker, A.H.[Amir Hossein],
Mersa, O.[Omid],
Sadeghi, M.A.[Mohammad Amin],
Rastegari, M.[Mohammad],
Araabi, B.N.[Babak N.],
Assisted Excitation of Activations:
A Learning Technique to Improve Object Detectors,
CVPR19(9193-9202).
IEEE DOI
2002
BibRef
Wang, X.D.[Xu-Dong],
Cai, Z.W.[Zhao-Wei],
Gao, D.[Dashan],
Vasconcelos, N.M.[Nuno M.],
Towards Universal Object Detection by Domain Attention,
CVPR19(7281-7290).
IEEE DOI
2002
BibRef
Li, R.[Rundong],
Wang, Y.[Yan],
Liang, F.[Feng],
Qin, H.W.[Hong-Wei],
Yan, J.[Junjie],
Fan, R.[Rui],
Fully Quantized Network for Object Detection,
CVPR19(2805-2814).
IEEE DOI
2002
BibRef
Carrilho, A.C.,
Galo, M.,
Automatic Object Extraction From High Resolution Aerial Imagery With
Simple Linear Iterative Clustering and Convolutional Neural Networks,
PIA19(61-66).
DOI Link
1912
BibRef
Cho, S.[Sungmin],
Choi, B.[Bowon],
Kim, D.H.[Do-Hwi],
Kwon, J.[Junseok],
Multi-Domain Attentive Detection Network,
ICIP19(2194-2198)
IEEE DOI
1910
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)
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
BibRef
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
BibRef
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
BibRef
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
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
BibRef
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
BibRef
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
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 .