He, S.F.[Sheng-Feng],
Lau, R.W.H.[Rynson W.H.],
Liu, W.X.[Wen-Xi],
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, 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
Long, Y.[Yang],
Zhai, X.F.[Xiao-Fang],
Wan, Q.[Qiao],
Tan, X.W.[Xiao-Wei],
Object Localization in Weakly Labeled Remote Sensing Images Based on
Deep Convolutional Features,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Pang, S.M.[Shan-Min],
Zhu, J.H.[Ji-Hua],
Wang, J.X.[Jia-Xing],
Ordonez, V.[Vicente],
Xue, J.R.[Jian-Ru],
Building discriminative CNN image representations for object
retrieval using the replicator equation,
PR(83), 2018, pp. 150-160.
Elsevier DOI
1808
Object retrieval, Replicator equation, Deep feature selection,
Deep feature weighting
BibRef
Yousif, H.[Hayder],
Yuan, J.[Jianhe],
Kays, R.[Roland],
He, Z.H.[Zhi-Hai],
Object detection from dynamic scene using joint background modeling
and fast deep learning classification,
JVCIR(55), 2018, pp. 802-815.
Elsevier DOI
1809
BibRef
Earlier: A1, A4, A3, Only:
Object segmentation in the deep neural network feature domain from
highly cluttered natural scenes,
ICIP17(3095-3099)
IEEE DOI
1803
Human-animal detection, Camera-trap images,
Background subtraction, Deep convolutional neural networks,
Wildlife monitoring.
Animals, Computational modeling, Feature extraction,
Image representation, Image segmentation, Proposals, Semantics,
object detection
BibRef
Chen, T.[Tao],
Lu, S.J.[Shi-Jian],
Fan, J.Y.[Jia-Yuan],
S-CNN: Subcategory-Aware Convolutional Networks for Object Detection,
PAMI(40), No. 10, October 2018, pp. 2522-2528.
IEEE DOI
1809
Detectors, Training, Object detection, Proposals, Feature extraction,
Robustness, Deformable models, Subcategory, object detection,
subcategory-aware CNN
BibRef
Fu, H.[Huan],
Gong, M.M.[Ming-Ming],
Wang, C.H.[Chao-Hui],
Tao, D.C.[Da-Cheng],
MoE-SPNet: A mixture-of-experts scene parsing network,
PR(84), 2018, pp. 226-236.
Elsevier DOI
1809
Scene parsing, Mixture-of-experts, Attention, Convolutional neural network
BibRef
Lim, L.A.[Long Ang],
Keles, H.Y.[Hacer Yalim],
Foreground segmentation using convolutional neural networks for
multiscale feature encoding,
PRL(112), 2018, pp. 256-262.
Elsevier DOI
1809
Foreground segmentation, Background subtraction, Deep learning,
Convolutional neural networks, Video surveillance, Pixel classification
BibRef
Wang, Y.[Yida],
Deng, W.H.[Wei-Hong],
Generative Model With Coordinate Metric Learning for Object
Recognition Based on 3D Models,
IP(27), No. 12, December 2018, pp. 5813-5826.
IEEE DOI
1810
BibRef
Earlier:
Self-restraint object recognition by model based CNN learning,
ICIP16(654-658)
IEEE DOI
1610
belief networks, feature extraction, image reconstruction,
image representation, image segmentation,
metric learning
Data models
BibRef
Zhu, Y.S.[You-Song],
Zhao, C.Y.[Chao-Yang],
Guo, H.Y.[Hai-Yun],
Wang, J.Q.[Jin-Qiao],
Zhao, X.[Xu],
Lu, H.Q.[Han-Qing],
Attention CoupleNet: Fully Convolutional Attention Coupling Network
for Object Detection,
IP(28), No. 1, January 2019, pp. 113-126.
IEEE DOI
1810
convolution, feature extraction, feedforward neural nets,
graph theory, image classification, image representation,
local parts
BibRef
Deng, Z.P.[Zhi-Peng],
Sun, H.[Hao],
Zhou, S.L.[Shi-Lin],
Zhao, J.P.[Juan-Ping],
Lei, L.[Lin],
Zou, H.X.[Huan-Xin],
Multi-scale object detection in remote sensing imagery with
convolutional neural networks,
PandRS(145), 2018, pp. 3-22.
Elsevier DOI
1810
Object detection, Deep learning, Convolutional neural networks,
Multi-modal remote sensing images
BibRef
Xiao, F.[Fen],
Deng, W.Z.[Wen-Zheng],
Peng, L.C.[Liang-Chan],
Cao, C.H.[Chun-Hong],
Hu, K.[Kai],
Gao, X.P.[Xie-Ping],
Multi-scale deep neural network for salient object detection,
IET-IPR(12), No. 11, November 2018, pp. 2036-2041.
DOI Link
1810
BibRef
Shuai, B.,
Ding, H.,
Liu, T.,
Wang, G.,
Jiang, X.,
Toward Achieving Robust Low-Level and High-Level Scene Parsing,
IP(28), No. 3, March 2019, pp. 1378-1390.
IEEE DOI
1812
feedforward neural nets, image representation,
image segmentation, object detection, segmentation network,
skip layers
BibRef
Sun, F.C.[Fu-Chun],
Kong, T.[Tao],
Huang, W.B.[Wen-Bing],
Tan, C.Q.[Chuan-Qi],
Fang, B.[Bin],
Liu, H.P.[Hua-Ping],
Feature Pyramid Reconfiguration With Consistent Loss for Object
Detection,
IP(28), No. 10, October 2019, pp. 5041-5051.
IEEE DOI
1909
BibRef
Earlier: A2, A1, A3, A6, Only:
Deep Feature Pyramid Reconfiguration for Object Detection,
ECCV18(VI: 172-188).
Springer DOI
1810
Object detection, Detectors, Feature extraction, Semantics, Training,
Proposals, Entropy, Accurate object detection,
consistent loss
BibRef
Li, Y.S.[Yan-Sheng],
Zhang, Y.J.[Yong-Jun],
Huang, X.[Xin],
Yuille, A.L.[Alan L.],
Deep networks under scene-level supervision for multi-class
geospatial object detection from remote sensing images,
PandRS(146), 2018, pp. 182-196.
Elsevier DOI
1812
Multi-class geospatial object detection, Deep networks,
Scene-level supervision,
Class-specific activation weights
BibRef
Xiong, S.Z.[Sheng-Zhou],
Tan, Y.H.[Yi-Hua],
Li, Y.S.[Yan-Sheng],
Wen, C.[Cai],
Yan, P.[Pei],
Subtask Attention Based Object Detection in Remote Sensing Images,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link
2105
BibRef
Li, Y.S.[Yan-Sheng],
Zhang, Y.J.[Yong-Jun],
Zhu, Z.H.[Zhi-Hui],
Error-Tolerant Deep Learning for Remote Sensing Image Scene
Classification,
Cyber(51), No. 4, April 2021, pp. 1756-1768.
IEEE DOI
2103
Remote sensing, Noise measurement, Machine learning, Robustness,
Collaboration, Error correction,
RSSC-oriented error-tolerant deep learning (RSSC-ETDL)
BibRef
Sangineto, E.[Enver],
Nabi, M.[Moin],
Culibrk, D.[Dubravko],
Sebe, N.[Nicu],
Self Paced Deep Learning for Weakly Supervised Object Detection,
PAMI(41), No. 3, March 2019, pp. 712-725.
IEEE DOI
1902
Training, Protocols, Object detection, Reliability, Task analysis,
Machine learning, Detectors, Weakly supervised learning,
training protocol
BibRef
Soviany, P.[Petru],
Ionescu, R.T.[Radu Tudor],
Rota, P.[Paolo],
Sebe, N.[Nicu],
Curriculum self-paced learning for cross-domain object detection,
CVIU(204), 2021, pp. 103166.
Elsevier DOI
2102
Object detection, Cross-domain, Unsupervised domain adaptation,
Curriculum learning, Self-paced learning
BibRef
Zhuo, X.Y.[Xiang-Yu],
Fraundorfer, F.[Friedrich],
Kurz, F.[Franz],
Reinartz, P.[Peter],
Automatic Annotation of Airborne Images by Label Propagation Based on
a Bayesian-CRF Model,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link
1902
Annotation for deep learning input.
BibRef
Zhu, J.[Jun],
Zhu, J.C.[Jiang-Cheng],
Wan, X.D.[Xu-Dong],
Wu, C.[Chao],
Xu, C.[Chao],
Object detection and localization in 3D environment by fusing raw
fisheye image and attitude data,
JVCIR(59), 2019, pp. 128-139.
Elsevier DOI
1903
Object detection, Deep learning, Data fusion, Fisheye camera,
Micro aerial vehicle, Localization
BibRef
Siméoni, O.[Oriane],
Iscen, A.[Ahmet],
Tolias, G.[Giorgos],
Avrithis, Y.[Yannis],
Chum, O.[Ondrej],
Graph-based particular object discovery,
MVA(30), No. 2, March 2019, pp. 243-254.
Springer DOI
1904
BibRef
Earlier:
Unsupervised Object Discovery for Instance Recognition,
WACV18(1745-1754)
IEEE DOI
1806
With background clutter.
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
Hsu, K.,
Lin, Y.,
Chuang, Y.,
Weakly Supervised Salient Object Detection by Learning A
Classifier-Driven Map Generator,
IP(28), No. 11, November 2019, pp. 5435-5449.
IEEE DOI
1909
Saliency detection, Generators, Training data, Training,
Feature extraction, Proposals, Task analysis,
weakly supervised learning
BibRef
Peng, H.Y.[Han-Yu],
Chen, S.F.[Shi-Feng],
BDNN: Binary convolution neural networks for fast object detection,
PRL(125), 2019, pp. 91-97.
Elsevier DOI
1909
Deep learning, Object detection, Network compression
BibRef
López-Tapia, S.,
Molina, R.,
de la Blanca, N.P.,
Deep CNNs for Object Detection Using Passive Millimeter Sensors,
CirSysVideo(29), No. 9, September 2019, pp. 2580-2589.
IEEE DOI
1909
Image segmentation, Feature extraction,
Convolution, Image sensors, Sensors, Classification, deep learning, security
BibRef
Song, X.,
Jiang, S.,
Wang, B.,
Chen, C.,
Chen, G.,
Image Representations With Spatial Object-to-Object Relations for
RGB-D Scene Recognition,
IP(29), No. 1, 2020, pp. 525-537.
IEEE DOI
1910
image classification, image coding, image representation,
object detection, recurrent neural nets, tensors,
BibRef
Song, K.,
Yang, H.,
Yin, Z.,
Multi-Scale Attention Deep Neural Network for Fast Accurate Object
Detection,
CirSysVideo(29), No. 10, October 2019, pp. 2972-2985.
IEEE DOI
1910
data mining, feature extraction,
learning (artificial intelligence), neural nets, deep neural network
BibRef
Lee, H.,
Eum, S.,
Kwon, H.,
ME R-CNN: Multi-Expert R-CNN for Object Detection,
IP(29), No. , 2020, pp. 1030-1044.
IEEE DOI
1911
Training, Object detection, Shape, Optimization, Task analysis,
Pipelines, Multiple experts, expert assigner
BibRef
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
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
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
Dong, Z.P.[Zhi-Peng],
Wang, M.[Mi],
Wang, Y.L.[Yan-Li],
Liu, Y.X.[Yan-Xiong],
Feng, Y.K.[Yi-Kai],
Xu, W.X.[Wen-Xue],
Multi-Oriented Object Detection in High-Resolution Remote Sensing
Imagery Based on Convolutional Neural Networks with Adaptive Object
Orientation Features,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Cherloo, M.N.[Mohammad Norizadeh],
Shiri, M.[Milad],
Daliri, M.R.[Mohammad Reza],
An enhanced HMAX model in combination with SIFT algorithm for object
recognition,
SIViP(14), No. 2, March 2020, pp. 425-433.
Springer DOI
2003
Hierarchical model and X (a feedforward network).
BibRef
Zhang, Z.W.[Zhe-Wei],
Jing, T.[Tao],
Tian, C.H.[Chun-Hua],
Cui, P.F.[Peng-Fei],
Li, X.J.[Xue-Jing],
Gao, M.L.[Mei-Lin],
Objects Discovery Based on Co-Occurrence Word Model With Anchor-Box
Polishing,
CirSysVideo(30), No. 3, March 2020, pp. 632-645.
IEEE DOI
2003
Training, Visualization, Deep learning, Computational modeling,
Feature extraction, Principal component analysis, LDA, region of interest
BibRef
Zhu, X.Y.[Xin-Yu],
Zhang, J.[Jun],
Chen, G.S.[Geng-Sheng],
ASAN: Self-Attending and Semantic Activating Network towards Better
Object Detection,
IEICE(E103-D), No. 3, March 2020, pp. 648-659.
WWW Link.
2003
BibRef
Feng, M.,
Lu, H.,
Yu, Y.,
Residual Learning for Salient Object Detection,
IP(29), 2020, pp. 4696-4708.
IEEE DOI
2003
Object detection, Feature extraction, Image reconstruction,
Visualization, Task analysis, Image segmentation,
deep learning
BibRef
Huang, K.[Kun],
Gao, S.H.[Sheng-Hua],
Image saliency detection via multi-scale iterative CNN,
VC(36), No. 7, July 2020, pp. 1355-1367.
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BibRef
Wu, M.H.[Ming-Hu],
Yue, H.H.[Han-Hui],
Wang, J.[Juan],
Huang, Y.X.[Yong-Xi],
Liu, M.[Min],
Jiang, Y.[Yuhan],
Ke, C.[Cong],
Zeng, C.[Cheng],
Object detection based on RGC mask R-CNN,
IET-IPR(14), No. 8, 19 June 2020, pp. 1502-1508.
DOI Link
2005
BibRef
Yuan, J.[Jin],
Xiong, H.C.[Heng-Chang],
Xiao, Y.[Yi],
Guan, W.[Weili],
Wang, M.[Meng],
Hong, R.[Richang],
Li, Z.Y.[Zhi-Yong],
Gated CNN: Integrating multi-scale feature layers for object
detection,
PR(105), 2020, pp. 107131.
Elsevier DOI
2006
Gated CNN, object detection, multi-scale feature layers, explainable CNN
BibRef
Zhou, C.,
Yuan, J.,
Occlusion Pattern Discovery for Object Detection and Occlusion
Reasoning,
CirSysVideo(30), No. 7, July 2020, pp. 2067-2080.
IEEE DOI
2007
Cognition, Object detection, Deformable models, Mixture models,
Detectors, Pattern matching, Adaptation models,
Faster R-CNN
BibRef
Xie, S.R.[Shao-Rong],
Liu, C.[Chang],
Gao, J.T.[Jian-Tao],
Li, X.M.[Xiao-Mao],
Luo, J.[Jun],
Fan, B.J.[Bao-Jie],
Chen, J.H.[Jia-Hong],
Pu, H.Y.[Hua-Yan],
Peng, Y.[Yan],
Diverse receptive field network with context aggregation for fast
object detection,
JVCIR(70), 2020, pp. 102770.
Elsevier DOI
2007
Object detection, Convolutional neural network,
Context aggregation, Multi-scale contextual representations
BibRef
He, X.,
Bai, S.,
Chu, J.,
Bai, X.,
An Improved Multi-View Convolutional Neural Network for 3D Object
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IP(29), 2020, pp. 7917-7930.
IEEE DOI
2007
Shape, Feature extraction, Measurement,
Training, Convolutional neural networks, Task analysis, multi-view CNN
BibRef
Xu, D.L.[Dong-Li],
Guan, J.[Jian],
Feng, P.M.[Peng-Ming],
Wang, W.W.[Wen-Wu],
Association Loss for Visual Object Detection,
SPLetters(27), 2020, pp. 1435-1439.
IEEE DOI
2009
Object detection, Detectors, Training, Heating systems,
Feature extraction, Visualization, Convolutional neural networks,
convolutional neural networks
BibRef
Chen, C.,
Zhang, Y.,
Lv, Q.,
Wei, S.,
Wang, X.,
Sun, X.,
Dong, J.,
RRNet:
A Hybrid Detector for Object Detection in Drone-Captured Images,
VisDrone19(100-108)
IEEE DOI
2004
image capture, learning (artificial intelligence), neural nets,
object detection, regression analysis, Deep Learning
BibRef
Azadi, S.[Samaneh],
Pathak, D.[Deepak],
Ebrahimi, S.[Sayna],
Darrell, T.J.[Trevor J.],
Compositional GAN: Learning Image-Conditional Binary Composition,
IJCV(128), No. 10-11, November 2020, pp. 2570-2585.
Springer DOI
2009
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
Convolutional codes, Kernel,
Object detection, Proposals, Semantics, Training
BibRef
Liu, C.,
Zhou, W.,
Chen, Y.,
Lei, J.,
Asymmetric Deeply Fused Network for Detecting Salient Objects in
RGB-D Images,
SPLetters(27), 2020, pp. 1620-1624.
IEEE DOI
2010
Feature extraction, Decoding, Convolution, Adaptation models, Fuses,
Visualization, Object detection, RGB-D,
adaptive attention transformer module
BibRef
Zhang, R.Q.[Rui-Qian],
Shao, Z.F.[Zhen-Feng],
Huang, X.[Xiao],
Wang, J.M.[Jia-Ming],
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Object Detection in UAV Images via Global Density Fused Convolutional
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RS(12), No. 19, 2020, pp. xx-yy.
DOI Link
2010
BibRef
Chen, S.,
Li, Z.,
Tang, Z.,
Relation R-CNN:
A Graph Based Relation-Aware Network for Object Detection,
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IEEE DOI
1806
Semantics, Object detection, Proposals, Automobiles, Detectors,
Visualization, Feature extraction, Object detection,
spatial relation
BibRef
Guo, W.[Wei],
Li, W.H.[Wei-Hong],
Li, Z.H.[Zheng-Hao],
Gong, W.G.[Wei-Guo],
Cui, J.K.[Jin-Kai],
Wang, X.R.[Xin-Ran],
A Slimmer Network with Polymorphic and Group Attention Modules for
More Efficient Object Detection in Aerial Images,
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DOI Link
2011
BibRef
Deng, S.T.[Su-Tao],
Li, S.A.[Shu-Ai],
Xie, K.[Ke],
Song, W.F.[Wen-Feng],
Liao, X.[Xiao],
Hao, A.M.[Ai-Min],
Qin, H.[Hong],
A Global-Local Self-Adaptive Network for Drone-View Object Detection,
IP(30), 2021, pp. 1556-1569.
IEEE DOI
2101
Detectors, Object detection, Training, Training data, Proposals,
Feature extraction, Convolution, Drone-view object detection,
coarse-to-fine adaptive detector
BibRef
Zhang, S.,
Wen, L.,
Lei, Z.,
Li, S.Z.,
RefineDet++: Single-Shot Refinement Neural Network for Object
Detection,
CirSysVideo(31), No. 2, February 2021, pp. 674-687.
IEEE DOI
2102
Object detection, Feature extraction, Detectors, Convolution,
Proposals, Neural networks, Training, Object detection, one-stage,
refinement network
BibRef
Zhang, S.,
Wen, L.,
Bian, X.,
Lei, Z.,
Li, S.Z.,
Single-Shot Refinement Neural Network for Object Detection,
CVPR18(4203-4212)
IEEE DOI
1812
Object detection, Detectors, Feature extraction, Task analysis,
Training, Convolution, Search problems
BibRef
Hassanzadeh, T.[Tahereh],
Essam, D.[Daryl],
Sarker, R.[Ruhul],
2D to 3D Evolutionary Deep Convolutional Neural Networks for Medical
Image Segmentation,
MedImg(40), No. 2, February 2021, pp. 712-721.
IEEE DOI
2102
BibRef
Earlier:
Evolutionary Attention Network for Medical Image Segmentation,
DICTA20(1-8)
IEEE DOI
2201
Biomedical imaging, Image segmentation, Encoding,
neuroevolution.
Training, Network topology, Neural networks,
Task analysis, Genetic algorithms
BibRef
Sun, X.[Xian],
Wang, P.J.[Pei-Jin],
Wang, C.[Cheng],
Liu, Y.F.[Ying-Fei],
Fu, K.[Kun],
PBNet: Part-based convolutional neural network for complex composite
object detection in remote sensing imagery,
PandRS(173), 2021, pp. 50-65.
Elsevier DOI
2102
Object detection, Remote sensing imagery,
Complex composite object, Part-based detection, Context information
BibRef
Chen, X.,
Li, H.,
Wu, Q.,
Ngan, K.N.,
Xu, L.,
High-Quality R-CNN Object Detection Using Multi-Path Detection
Calibration Network,
CirSysVideo(31), No. 2, February 2021, pp. 715-727.
IEEE DOI
2102
Proposals, Detectors, Feature extraction, Calibration,
Object detection, Benchmark testing, Neural networks,
object recognition
BibRef
Song, L.Y.[Ling-Yun],
Liu, J.[Jun],
Sun, M.X.[Ming-Xuan],
Shang, X.Q.[Xue-Qun],
Weakly Supervised Group Mask Network for Object Detection,
IJCV(129), No. 3, March 2021, pp. 681-702.
Springer DOI
2103
BibRef
Huang, W.[Wei],
Li, G.Y.[Guan-Yi],
Chen, Q.Q.[Qi-Qiang],
Ju, M.[Ming],
Qu, J.T.[Jian-Tao],
CF2PN: A Cross-Scale Feature Fusion Pyramid Network Based Remote
Sensing Target Detection,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link
2103
BibRef
Yuan, Z.C.[Zhi-Chao],
Liu, Z.M.[Zi-Ming],
Zhu, C.B.[Chun-Bo],
Qi, J.[Jing],
Zhao, D.[Danpei],
Object Detection in Remote Sensing Images via Multi-Feature Pyramid
Network with Receptive Field Block,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link
2103
BibRef
Li, Z.H.[Zhi-Hang],
Xi, T.[Teng],
Zhang, G.[Gang],
Liu, J.T.[Jing-Tuo],
He, R.[Ran],
AutoDet: Pyramid Network Architecture Search for Object Detection,
IJCV(129), No. 4, April 2021, pp. 1087-1105.
Springer DOI
2104
BibRef
Cai, Z.W.[Zhao-Wei],
Vasconcelos, N.M.[Nuno M.],
Cascade R-CNN: High Quality Object Detection and Instance
Segmentation,
PAMI(43), No. 5, May 2021, pp. 1483-1498.
IEEE DOI
2104
BibRef
Earlier:
Cascade R-CNN: Delving Into High Quality Object Detection,
CVPR18(6154-6162)
IEEE DOI
1812
Detectors, Object detection, Training, Proposals, Task analysis,
Feature extraction, Object detection,
instance segmentation.
Noise measurement
BibRef
Li, Z.[Zun],
Lang, C.[Congyan],
Liew, J.H.[Jun Hao],
Li, Y.D.[Yi-Dong],
Hou, Q.B.[Qi-Bin],
Feng, J.S.[Jia-Shi],
Cross-Layer Feature Pyramid Network for Salient Object Detection,
IP(30), 2021, pp. 4587-4598.
IEEE DOI
2105
Feature extraction, Object detection, Semantics,
Saliency detection, Fuses, Visualization,
progressive feature fusion
BibRef
Sharma, V.[Vipul],
Mir, R.N.[Roohie Naaz],
Maximum entropy-based semi-supervised learning for automatic detection
and recognition of objects using deep ConvNets,
IJCVR(11), No. 3, 2021, pp. 328-356.
DOI Link
2106
BibRef
Shuang, K.[Kai],
Lyu, Z.H.[Zhi-Heng],
Loo, J.[Jonathan],
Zhang, W.T.[Wen-Tao],
Scale-balanced loss for object detection,
PR(117), 2021, pp. 107997.
Elsevier DOI
2106
Object detection, Neural network, Matching imbalance
BibRef
Jiang, Y.Y.[Yin-Yin],
Li, M.[Ming],
Zhang, P.[Peng],
Tan, X.F.[Xiao-Feng],
Song, W.Y.[Wan-Ying],
Hierarchical fusion convolutional neural networks for SAR image
segmentation,
PRL(147), 2021, pp. 115-123.
Elsevier DOI
2106
Synthetic aperture radar, Image segmentation,
Hierarchical fusion convolutional neural networks,
Dempster-Shafer evidential theory
BibRef
Girum, K.B.[Kibrom Berihu],
Créhange, G.[Gilles],
Lalande, A.[Alain],
Learning With Context Feedback Loop for Robust Medical Image
Segmentation,
MedImg(40), No. 6, June 2021, pp. 1542-1554.
IEEE DOI
2106
Image segmentation, Feature extraction, Biomedical imaging, Shape,
Decoding, Computed tomography, Feedback loop, CNN, feedback loop, MRI,
CT
BibRef
Zhu, B.[Bin],
Song, Q.[Qing],
Yang, L.[Lu],
Wang, Z.H.[Zhi-Hui],
Liu, C.[Chun],
Hu, M.J.[Meng-Jie],
CPM R-CNN: Calibrating Point-guided Misalignment in Object Detection,
WACV21(3247-3256)
IEEE DOI
2106
Location awareness, Heating systems,
Estimation, Object detection, Feature extraction
BibRef
Wang, K.[Kun],
Liu, M.Z.[Mao-Zhen],
A feature-optimized Faster regional convolutional neural network for
complex background objects detection,
IET-IPR(15), No. 2, 2021, pp. 378-392.
DOI Link
2106
BibRef
Wang, Q.[Qiang],
Zhou, L.[Lukuan],
Yao, Y.C.[Yun-Cong],
Wang, Y.[Yong],
Li, J.[Jun],
Yang, W.K.[Wan-Kou],
An Interconnected Feature Pyramid Networks for object detection,
JVCIR(79), 2021, pp. 103260.
Elsevier DOI
2109
Attention mechanism, Feature Pyramid Networks,
Object detection, Deep learning
BibRef
Zhang, L.[Lei],
Wang, Y.H.[Yue-Huan],
Huo, Y.[Yang],
Object Detection in High-Resolution Remote Sensing Images Based on a
Hard-Example-Mining Network,
GeoRS(59), No. 10, October 2021, pp. 8768-8780.
IEEE DOI
2109
Feature extraction, Object detection, Proposals, Remote sensing,
Training, Detectors, Task analysis, remote sensing images (RSIs)
BibRef
Aziz, L.[Lubna],
FC, M.S.B.[Md. Sah Bin_Haji_Salam],
Ayub, S.[Sara],
Multi-level refinement enriched feature pyramid network for object
detection,
IVC(115), 2021, pp. 104287.
Elsevier DOI
2110
CNN, Object detection, Chained parallel pooling, Feature pyramid
BibRef
Obeso, A.M.[Abraham Montoya],
Benois-Pineau, J.[Jenny],
García Vázquez, M.S.[Mireya Saraí],
Acosta, A.Á.R.[Alejandro Álvaro Ramírez],
Visual vs internal attention mechanisms in deep neural networks for
image classification and object detection,
PR(123), 2022, pp. 108411.
Elsevier DOI
2112
Deep learning, Image classification, Object detection,
Visual attention, Saliency maps
BibRef
Li, Z.W.[Zhang-Wei],
Hu, A.[Anshun],
Wang, X.F.[Xiao-Fei],
Hu, J.[Jun],
Zhang, G.J.[Gui-Jun],
Learning to capture dependencies between global features of different
convolution layers,
JVCIR(81), 2021, pp. 103360.
Elsevier DOI
2112
Deep learning, Object detection, Non-local neural network,
Global features dependencies
BibRef
Wang, D.[Di],
Du, B.[Bo],
Zhang, L.P.[Liang-Pei],
Fully Contextual Network for Hyperspectral Scene Parsing,
GeoRS(60), 2022, pp. 1-16.
IEEE DOI
2112
Feature extraction, Hyperspectral imaging, Convolution,
Task analysis, Recurrent neural networks,
scale attention module (SAM)
BibRef
Zhao, J.[Junhe],
Xu, S.[Sheng],
Wang, R.[Runqi],
Zhang, B.C.[Bao-Chang],
Guo, G.D.[Guo-Dong],
Doermann, D.[David],
Sun, D.[Dianmin],
Data-adaptive binary neural networks for efficient object detection
and recognition,
PRL(153), 2022, pp. 239-245.
Elsevier DOI
2201
Deep learning, Model compression, Binary neural networks,
Object detection, Object recognition
BibRef
Tian, Z.[Zhi],
Shen, C.H.[Chun-Hua],
Chen, H.[Hao],
He, T.[Tong],
FCOS: A Simple and Strong Anchor-Free Object Detector,
PAMI(44), No. 4, April 2022, pp. 1922-1933.
IEEE DOI
2203
Detectors, Task analysis, Object detection, Training, Head,
Magnetic heads, Semantics, Object detection, deep learning
BibRef
Shao, C.Y.[Chun-Yan],
Zhang, L.M.[Li-Min],
Pan, W.[Wang],
Faster R-CNN Learning-Based Semantic Filter for Geometry Estimation
and Its Application in vSLAM Systems,
ITS(23), No. 6, June 2022, pp. 5257-5266.
IEEE DOI
2206
Semantics, Geometry, Estimation, Feature extraction, Visualization,
Task analysis, Epipolar geometry, computer vision system,
semantic filter
BibRef
Liu, H.Z.[Hong-Zhe],
Wang, N.W.[Ning-Wei],
Li, X.W.[Xue-Wei],
Xu, C.[Cheng],
Li, Y.Z.[Ya-Ze],
BFF R-CNN: Balanced Feature Fusion for Object Detection,
IEICE(E105-D), No. 8, August 2022, pp. 1472-1480.
WWW Link.
2207
BibRef
Zheng, Y.C.[Yu-Chao],
Zhang, X.X.[Xin-Xin],
Zhang, R.[Rui],
Wang, D.[Dahan],
Gated Path Aggregation Feature Pyramid Network for Object Detection
in Remote Sensing Images,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Bo, Q.[Qihan],
Ma, W.[Wei],
Lai, Y.K.[Yu-Kun],
Zha, H.B.[Hong-Bin],
All-Higher-Stages-In Adaptive Context Aggregation for Semantic Edge
Detection,
CirSysVideo(32), No. 10, October 2022, pp. 6778-6791.
IEEE DOI
2210
Semantics, Image edge detection, Feature extraction, Open systems,
Image segmentation, Horses, Aggregates, Semantic edge detection,
object-level semantic integration
BibRef
Zhang, X.[Xiuwei],
Guo, W.[Wei],
Xing, Y.H.[Ying-Hui],
Wang, W.[Wenna],
Yin, H.L.[Han-Lin],
Zhang, Y.N.[Yan-Ning],
AugFCOS: Augmented fully convolutional one-stage object detection
network,
PR(134), 2023, pp. 109098.
Elsevier DOI
2212
Feature pyramid network, Object detection, Sample selection, Attention module
BibRef
Liang, T.T.[Ting-Ting],
Chu, X.J.[Xiao-Jie],
Liu, Y.D.[Yu-Dong],
Wang, Y.T.[Yong-Tao],
Tang, Z.[Zhi],
Chu, W.[Wei],
Chen, J.D.[Jing-Dong],
Ling, H.B.[Hai-Bin],
CBNet: A Composite Backbone Network Architecture for Object Detection,
IP(31), 2022, pp. 6893-6906.
IEEE DOI
2212
Detectors, Object detection, Feature extraction,
Computer architecture, Training, Transformers, Head, Deep learning,
composite architectures
BibRef
Chen, J.J.[Juan-Juan],
Hong, H.S.[Han-Sheng],
Song, B.[Bin],
Guo, J.[Jie],
Chen, C.[Chen],
Xu, J.J.[Jun-Jie],
MDCT: Multi-Kernel Dilated Convolution and Transformer for One-Stage
Object Detection of Remote Sensing Images,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link
2301
BibRef
He, Z.W.[Zhen-Wei],
Zhang, L.[Lei],
Gao, X.B.[Xin-Bo],
Zhang, D.[David],
Multi-adversarial Faster-RCNN with Paradigm Teacher for Unrestricted
Object Detection,
IJCV(131), No. 3, March 2023, pp. 680-700.
Springer DOI
2302
BibRef
Earlier: A1, A2, Only:
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
Huang, H.[Hao],
Li, L.L.[Liang-Liang],
Ma, H.B.[Hong-Bing],
An Improved Cascade R-CNN-Based Target Detection Algorithm for UAV
Aerial Images,
ICIVC22(232-237)
IEEE DOI
2301
Photography, Location awareness, Image recognition,
Target recognition, Fuses, Object detection, cascade detection
BibRef
Cheng, Z.[Zhen],
Xiong, J.S.[Jian-She],
Yang, P.C.[Peng-Cheng],
Yang, K.[Kai],
Chen, Y.N.[Yun-Nuo],
Object Detection in Optical Remote Sensing Images Based on Improved
Lightweight Neural Network,
ICIVC22(152-157)
IEEE DOI
2301
Neural networks, Transfer learning, Transportation,
Object detection, Optical imaging, Feature extraction, transfer learning
BibRef
Guo, N.[Nan],
Luan, S.[Sike],
Li, J.Y.[Jing-Yuan],
An Optimization Scheme of Object Detection Model Based on CNN Feature
Visualization Method,
ICIVC22(94-99)
IEEE DOI
2301
Training, Visualization, Analytical models, Computational modeling,
Object detection, Feature extraction, object detection, YOLOv3,
feature visualization
BibRef
Jung, H.[Harim],
Oh, M.S.[Myeong-Seok],
Yang, C.J.[Cheol-Jong],
Lee, S.W.[Seong-Whan],
Neural Architecture Adaptation for Object Detection by Searching
Channel Dimensions and Mapping Pre-trained Parameters,
ICPR22(2393-2400)
IEEE DOI
2212
Training, Costs, Object detection, Search problems, Task analysis
BibRef
Liu, Z.[Zhuang],
Mao, H.Z.[Han-Zi],
Wu, C.Y.[Chao-Yuan],
Feichtenhofer, C.[Christoph],
Darrell, T.J.[Trevor J.],
Xie, S.N.[Sai-Ning],
A ConvNet for the 2020s,
CVPR22(11966-11976)
IEEE DOI
2210
Image segmentation, Visualization, Computational modeling,
Scalability, Semantics, Transformers, Representation learning
BibRef
Hong, Q.H.[Qing-Hang],
Liu, F.M.[Feng-Ming],
Li, D.[Dong],
Liu, J.[Ji],
Tian, L.[Lu],
Shan, Y.[Yi],
Dynamic Sparse R-CNN,
CVPR22(4713-4722)
IEEE DOI
2210
Training, Convolution, Heuristic algorithms, Object detection,
Detectors, Transformers, Prediction algorithms,
Optimization methods
BibRef
Nguyen, D.K.[Duy-Kien],
Ju, J.H.[Ji-Hong],
Booij, O.[Olaf],
Oswald, M.R.[Martin R.],
Snoek, C.G.M.[Cees G. M.],
BoxeR: Box-Attention for 2D and 3D Transformers,
CVPR22(4763-4772)
IEEE DOI
2210
Codes, Object detection, Transformers, Pattern recognition,
Task analysis, Recognition: detection, categorization, retrieval,
grouping and shape analysis
BibRef
Li, Y.L.[Ya-Li],
Wang, S.J.[Sheng-Jin],
R(Det)2: Randomized Decision Routing for Object Detection,
CVPR22(4815-4824)
IEEE DOI
2210
Deep learning, Representation learning, Head, Statistical analysis,
Neural networks, Object detection, Routing, Recognition: detection,
Statistical methods
BibRef
Liu, Y.[Yanan],
Lu, Y.[Yao],
On-Sensor Binarized Fully Convolutional Neural Network for
Localisation and Coarse Segmentation,
EVW22(3628-3637)
IEEE DOI
2210
Performance evaluation, Heating systems, Convolution,
Neural networks, Object segmentation, Parallel processing
BibRef
Wu, W.[Wei],
Chang, H.[Hao],
Zheng, Y.H.[Yong-Hua],
Li, Z.[Zhu],
Chen, Z.W.[Zhi-Wen],
Zhang, Z.H.[Zi-Heng],
Contrastive Learning-based Robust Object Detection under Smoky
Conditions,
UG22(4294-4301)
IEEE DOI
2210
Training, Object detection, Detectors, Transforms, Prediction algorithms
BibRef
Cygert, S.[Sebastian],
Czyzewski, A.[Andrzej],
Robust Object Detection with Multi-input Multi-output Faster R-CNN,
CIAP22(I:572-583).
Springer DOI
2205
BibRef
Xie, X.X.[Xing-Xing],
Cheng, G.[Gong],
Wang, J.B.[Jia-Bao],
Yao, X.W.[Xi-Wen],
Han, J.W.[Jun-Wei],
Oriented R-CNN for Object Detection,
ICCV21(3500-3509)
IEEE DOI
2203
Head, Codes, Refining, Object detection, Detectors, Benchmark testing,
Detection and localization in 2D and 3D,
BibRef
Azam, B.[Basim],
Mandal, R.[Ranju],
Verma, B.[Brijesh],
Fully Convolutional Neural Network with Relation Aware Context
Information for Image Parsing,
DICTA21(01-06)
IEEE DOI
2201
Image segmentation, Adaptation models, Digital images, Semantics,
Neural networks, Image parsing
BibRef
Zhu, X.K.[Xing-Kui],
Lyu, S.C.[Shu-Chang],
Wang, X.[Xu],
Zhao, Q.[Qi],
TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for
Object Detection on Drone-Captured Scenarios,
VisDrone21(2778-2788)
IEEE DOI
2112
Location awareness, Navigation, Image color analysis,
Object detection, Detectors, Transformers, Magnetic heads
BibRef
Stäcker, L.[Lukas],
Fei, J.C.[Jun-Cong],
Heidenreich, P.[Philipp],
Bonarens, F.[Frank],
Rambach, J.[Jason],
Stricker, D.[Didier],
Stiller, C.[Christoph],
Deployment of Deep Neural Networks for Object Detection on Edge AI
Devices with Runtime Optimization,
ERCVAD21(1015-1022)
IEEE DOI
2112
Performance evaluation, Deep learning, Runtime,
Quantization (signal), Laser radar, Image edge detection
BibRef
Yang, Z.[Zuomin],
Wang, W.L.[Wan-Li],
An Effective Algorithm for Object Detection Based on Deep Learning,
ICIVC21(26-30)
IEEE DOI
2112
Deep learning, Training, Measurement, Neural networks,
Object detection, Detection algorithms, object detection, FIoU, neural network
BibRef
Zhang, T.Y.[Tian-Yi],
Lin, J.[Jie],
Hu, P.[Peng],
Zhao, B.[Bin],
Aly, M.M.S.[Mohamed M. Sabry],
PSRR-MaxpoolNMS:
Pyramid Shifted MaxpoolNMS with Relationship Recovery,
CVPR21(15835-15843)
IEEE DOI
2111
Non-maximum Suppression. CNN detection.
Pipelines, Detectors, Object detection, Hardware,
Pattern recognition, Convolutional neural networks
BibRef
Sun, P.Z.[Pei-Ze],
Zhang, R.F.[Ru-Feng],
Jiang, Y.[Yi],
Kong, T.[Tao],
Xu, C.F.[Chen-Feng],
Zhan, W.[Wei],
Tomizuka, M.[Masayoshi],
Li, L.[Lei],
Yuan, Z.H.[Ze-Huan],
Wang, C.H.[Chang-Hu],
Luo, P.[Ping],
Sparse R-CNN: End-to-End Object Detection with Learnable Proposals,
CVPR21(14449-14458)
IEEE DOI
2111
Training, Schedules, Head, Object detection, Detectors,
Pattern recognition, Proposals
BibRef
Yang, L.[Le],
Jiang, H.J.[Hao-Jun],
Cai, R.[Ruojin],
Wang, Y.L.[Yu-Lin],
Song, S.J.[Shi-Ji],
Huang, G.[Gao],
Tian, Q.[Qi],
CondenseNet V2: Sparse Feature Reactivation for Deep Networks,
CVPR21(3568-3577)
IEEE DOI
2111
Training, Computational modeling,
Object detection, Search problems, Pattern recognition
BibRef
Izquierdo-Cordova, R.[Ramon],
Mayol-Cuevas, W.[Walterio],
Filter Distribution Templates in Convolutional Networks for Image
Classification Tasks,
LXCV21(1241-1246)
IEEE DOI
2109
Computational modeling, Neural networks, Memory management,
Manuals, Data models, Pattern recognition, Convolutional neural networks
BibRef
Kechaou, A.[Amine],
Martinez, M.[Manuel],
Haurilet, M.[Monica],
Stiefelhagen, R.[Rainer],
Detective: An Attentive Recurrent Model for Sparse Object Detection,
ICPR21(5340-5347)
IEEE DOI
2105
Training, Visualization, Recurrent neural networks,
Object detection, Detectors, Predictive models, Decoding
BibRef
Hyeok, Y.J.[Yoo Jin],
Dongsuk, K.[Kum],
Won, C.J.[Choi Jun],
ScarfNet: Multi-scale Features with Deeply Fused and Redistributed
Semantics for Enhanced Object Detection,
ICPR21(4505-4512)
IEEE DOI
2105
Fuses, Semantics, Detectors, Object detection, Transforms,
Benchmark testing, Performance gain
BibRef
Chen, S.J.[Sheng-Jia],
Li, Z.X.[Zhi-Xin],
Huang, F.C.[Fei-Cheng],
Zhang, C.L.[Can-Long],
Ma, H.F.[Hui-Fang],
Object Detection Using Dual Graph Network,
ICPR21(3280-3287)
IEEE DOI
2105
Knowledge engineering, Visualization, Semantics, Directed graphs,
Object detection, Detectors, Feature extraction
BibRef
Zhang, J.C.[Jia-Cheng],
Zhao, Z.C.[Zhi-Cheng],
Su, F.[Fei],
Efficient-Receptive Field Block with Group Spatial Attention
Mechanism for Object Detection,
ICPR21(3248-3255)
IEEE DOI
2105
Radio frequency, Heating systems, Strips, Head, Semantics,
Object detection, Network architecture
BibRef
Ji, H.Q.[Hao-Qin],
Lu, W.Z.[Wei-Zeng],
Shen, L.L.[Lin-Lin],
Backbone Based Feature Enhancement for Object Detection,
ACCV20(III:56-70).
Springer DOI
2103
BibRef
Fang, W.H.[Wen-Hao],
Han, X.H.[Xian-Hua],
Spatial and Channel Attention Modulated Network for Medical Image
Segmentation,
MLCSA20(3-17).
Springer DOI
2103
BibRef
Sun, Y.,
Lin, S.,
Chen, L.,
A Novel Two-path Backbone Network for Object Detection,
CVIDL20(250-254)
IEEE DOI
2102
feature extraction, learning (artificial intelligence),
object detection, two-path backbone network.
BibRef
He, Z.W.[Zhen-Wei],
Zhang, L.[Lei],
Domain Adaptive Object Detection via Asymmetric Tri-Way Faster-RCNN,
ECCV20(XXIV:309-324).
Springer DOI
2012
BibRef
Zhang, H.K.[Hong-Kai],
Chang, H.[Hong],
Ma, B.P.[Bing-Peng],
Wang, N.Y.[Nai-Yan],
Chen, X.L.[Xi-Lin],
Dynamic R-CNN:
Towards High Quality Object Detection via Dynamic Training,
ECCV20(XV:260-275).
Springer DOI
2011
BibRef
Mei, R.,
Wang, H.,
Men, A.,
Attention-Enhanced And More Balanced R-CNN For Object Detection,
ICIP20(2136-2140)
IEEE DOI
2011
Convolution, Object detection, Proposals,
Graphics processing units, Task analysis, Standards, Transforms,
criss-cross attention module
BibRef
Miao, S.Y.[Shu-Yu],
Feng, R.[Rui],
Zhang, Y.J.[Yue-Jie],
Representation Reconstruction Head for Object Detection,
ICIP20(1516-1520)
IEEE DOI
2011
Head, Sensitivity, Feature extraction, Iron, Object detection,
Convolution, Fuses, Object detection,
location sensitivity enhancement representation
BibRef
Lu, X.[Xin],
Li, Q.Q.[Quan-Quan],
Li, B.[Buyu],
Yan, J.J.[Jun-Jie],
MimicDet: Bridging the Gap Between One-stage and Two-stage Object
Detection,
ECCV20(XIV:541-557).
Springer DOI
2011
BibRef
Wang, T.[Tong],
Zhu, Y.S.[You-Song],
Zhao, C.Y.[Chao-Yang],
Zeng, W.[Wei],
Wang, Y.W.[Yao-Wei],
Wang, J.Q.[Jin-Qiao],
Tang, M.[Ming],
Large Batch Optimization for Object Detection:
Training Coco in 12 minutes,
ECCV20(XXI:481-496).
Springer DOI
2011
BibRef
Zhou, D.Z.[Dong-Zhan],
Zhou, X.[Xinchi],
Zhang, H.[Hongwen],
Yi, S.[Shuai],
Ouyang, W.L.[Wan-Li],
Cheaper Pre-training Lunch: An Efficient Paradigm for Object Detection,
ECCV20(VIII:258-274).
Springer DOI
2011
BibRef
Zhao, X.Q.[Xiao-Qi],
Pang, Y.W.[You-Wei],
Zhang, L.[Lihe],
Lu, H.C.[Hu-Chuan],
Zhang, L.[Lei],
Suppress and Balance: A Simple Gated Network for Salient Object
Detection,
ECCV20(II:35-51).
Springer DOI
2011
BibRef
Amjoud, A.B.[Ayoub Benali],
Amrouch, M.[Mustapha],
Convolutional Neural Networks Backbones for Object Detection,
ICISP20(282-289).
Springer DOI
2009
BibRef
Ke, W.,
Zhang, T.,
Huang, Z.,
Ye, Q.,
Liu, J.,
Huang, D.,
Multiple Anchor Learning for Visual Object Detection,
CVPR20(10203-10212)
IEEE DOI
2008
Detectors, Training, Optimization, Object detection,
Maximum likelihood estimation, Linear programming, Visualization
BibRef
Liu, J.,
Hou, Q.,
Cheng, M.,
Wang, C.,
Feng, J.,
Improving Convolutional Networks With Self-Calibrated Convolutions,
CVPR20(10093-10102)
IEEE DOI
2008
Convolutional codes, Task analysis, Kernel,
Calibration, Standards, Object detection
BibRef
Singh, S.,
Krishnan, S.,
Filter Response Normalization Layer: Eliminating Batch Dependence in
the Training of Deep Neural Networks,
CVPR20(11234-11243)
IEEE DOI
2008
Training, Degradation, Neural networks,
Object detection, Task analysis, Testing
BibRef
Cai, Q.,
Pan, Y.,
Wang, Y.,
Liu, J.,
Yao, T.,
Mei, T.,
Learning a Unified Sample Weighting Network for Object Detection,
CVPR20(14161-14170)
IEEE DOI
2008
Detectors, Task analysis, Object detection, Training, Uncertainty,
Proposals, Training data
BibRef
Joung, S.,
Kim, S.,
Kim, H.,
Kim, M.,
Kim, I.,
Cho, J.,
Sohn, K.,
Cylindrical Convolutional Networks for Joint Object Detection and
Viewpoint Estimation,
CVPR20(14151-14160)
IEEE DOI
2008
Feature extraction, Estimation, Kernel,
Object recognition, Object detection
BibRef
Guo, C.,
Fan, B.,
Zhang, Q.,
Xiang, S.,
Pan, C.,
AugFPN: Improving Multi-Scale Feature Learning for Object Detection,
CVPR20(12592-12601)
IEEE DOI
2008
Feature extraction, Detectors, Semantics, Object detection,
Proposals, Convolution, Data mining
BibRef
Wang, A.T.[Ang-Tian],
Sun, Y.H.[Yi-Hong],
Kortylewski, A.,
Yuille, A.L.,
Robust Object Detection Under Occlusion With Context-Aware
CompositionalNets,
CVPR20(12642-12651)
IEEE DOI
2008
Robustness, Object detection, Machine learning, Estimation,
Task analysis, Convolutional neural networks, Context modeling
BibRef
Han, Y.,
Liu, X.,
Sheng, Z.,
Ren, Y.,
Han, X.,
You, J.,
Liu, R.,
Luo, Z.,
Wasserstein Loss based Deep Object Detection,
AutoDrive20(4299-4305)
IEEE DOI
2008
Object detection, Detectors, Feature extraction, Task analysis,
Proposals, Measurement, Machine learning
BibRef
Li, Y.,
Chen, Y.,
Wang, N.,
Zhang, Z.,
Scale-Aware Trident Networks for Object Detection,
ICCV19(6053-6062)
IEEE DOI
2004
Code, Object Detection.
WWW Link. feature extraction,
learning (artificial intelligence), neural nets,
Computer architecture
BibRef
Li, P.,
Chen, B.,
Ouyang, W.,
Wang, D.,
Yang, X.,
Lu, H.,
GradNet: Gradient-Guided Network for Visual Object Tracking,
ICCV19(6161-6170)
IEEE DOI
2004
convolutional neural nets, feature extraction, gradient methods,
object detection, object tracking, visual object tracking,
Real-time systems
BibRef
Xu, H.,
Yao, L.,
Li, Z.,
Liang, X.,
Zhang, W.,
Auto-FPN: Automatic Network Architecture Adaptation for Object
Detection Beyond Classification,
ICCV19(6648-6657)
IEEE DOI
2004
gradient methods, image classification,
object detection, NAS, CNN architecture, image classification, Training
BibRef
Yang, K.,
Li, D.,
Dou, Y.,
Towards Precise End-to-End Weakly Supervised Object Detection Network,
ICCV19(8371-8380)
IEEE DOI
2004
learning (artificial intelligence), object detection,
regression analysis, object position prediction, local minima,
BibRef
Peng, J.R.[Jun-Ran],
Sun, M.[Ming],
Zhang, Z.X.[Zhao-Xiang],
Tan, T.N.[Tie-Niu],
Yan, J.J.[Jun-Jie],
POD: Practical Object Detection With Scale-Sensitive Network,
ICCV19(9606-9615)
IEEE DOI
2004
image filtering, learning (artificial intelligence),
object detection, optimisation, practical object detection,
Optimization
BibRef
Tian, Z.,
Shen, C.,
Chen, H.,
He, T.,
FCOS: Fully Convolutional One-Stage Object Detection,
ICCV19(9626-9635)
IEEE DOI
2004
Code, Object Detection.
WWW Link. image segmentation, object detection, predefined anchor boxes,
final detection performance, pre-defined set, anchor box free,
Head
BibRef
Nie, J.,
Anwer, R.M.,
Cholakkal, H.,
Khan, F.S.,
Pang, Y.,
Shao, L.,
Enriched Feature Guided Refinement Network for Object Detection,
ICCV19(9536-9545)
IEEE DOI
2004
Code, Object Detection.
WWW Link. feature extraction, image classification,
learning (artificial intelligence), neural nets,
Benchmark testing
BibRef
Aghdam, H.H.,
Gonzalez-Garcia, A.,
Lopez, A.,
Weijer, J.,
Active Learning for Deep Detection Neural Networks,
ICCV19(3671-3679)
IEEE DOI
2004
learning (artificial intelligence), neural nets,
object detection, pedestrians, active learning,
BibRef
Zeng, Z.,
Liu, B.,
Fu, J.,
Chao, H.,
Zhang, L.,
WSOD2: Learning Bottom-Up and Top-Down Objectness Distillation for
Weakly-Supervised Object Detection,
ICCV19(8291-8299)
IEEE DOI
2004
convolutional neural nets, feature extraction,
image representation, learning (artificial intelligence),
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],
Miksik, O.[Ondrej],
Schönberger, J.L.[Johannes L.],
Pollefeys, M.[Marc],
Cross-Descriptor Visual Localization and Mapping,
ICCV21(6038-6047)
IEEE DOI
2203
Location awareness, Visualization, Mixed reality,
Benchmark testing, Sparks, Stereo,
Machine learning architectures and formulations
BibRef
Dusmanu, M.[Mihai],
Schönberger, J.L.[Johannes L.],
Pollefeys, M.[Marc],
Multi-view Optimization of Local Feature Geometry,
ECCV20(I:670-686).
Springer DOI
2011
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
Wang, H.Y.[Hui-Yu],
Zhu, Y.K.[Yu-Kun],
Green, B.[Bradley],
Adam, H.[Hartwig],
Yuille, A.L.[Alan L.],
Chen, L.C.[Liang-Chieh],
Axial-Deeplab: Stand-alone Axial-Attention for Panoptic Segmentation,
ECCV20(IV:108-126).
Springer DOI
2011
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.D.[Run-Dong],
Wang, Y.[Yan],
Liang, F.[Feng],
Qin, H.W.[Hong-Wei],
Yan, J.J.[Jun-Jie],
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
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
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.N.[Dimitris N>],
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.W.[Li-Wei],
Wei, Y.C.[Yi-Chen],
Dai, J.F.[Ji-Feng],
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
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, Data models, 3D Object Recognition
BibRef
Zhang, X.,
Chen, Y.,
Zhu, B.,
Wang, J.,
Tang, M.,
Lu, H.,
Tree Hierarchical CNNs for Object Parsing,
ICIP18(1588-1592)
IEEE DOI
1809
Image segmentation, Head, Semantics, Torso, Visualization,
Legged locomotion, Cows, object parsing, tree hierarchical CNNs,
part-aware fusion
BibRef
Fukagai, T.,
Maeda, K.,
Tanabe, S.,
Shirahata, K.,
Tomita, Y.,
Ike, A.,
Nakagawa, A.,
Speed-Up of Object Detection Neural Network with GPU,
ICIP18(301-305)
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
Sobti, A.,
Arora, C.,
Balakrishnan, M.,
Object Detection in Real-Time Systems: Going Beyond Precision,
WACV18(1020-1028)
IEEE DOI
1806
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).
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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).
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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
Feature extraction, Microprocessors,
Object detection, Proposals, Real-time systems, Semantics,
multi-feature
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
Convolutional neural networks, Head, Labeling, Machine learning,
spatial pyramid saliency map
BibRef
Zhang, J.,
Li, B.,
Dai, Y.,
Porikli, F.M.[Fatih M.],
He, M.,
Integrated deep and shallow networks for salient object detection,
ICIP17(1537-1541)
IEEE DOI
1803
Color, Convolutional neural networks, Machine learning,
Object detection, Saliency detection, Semantics, Training,
shallow network
BibRef
Li, T.,
Zhao, X.,
Cost efficient subcategory-aware CNN for object detection,
ICIP17(4202-4206)
IEEE DOI
1803
Automobiles, Benchmark testing, Heating systems, Neurons,
Object detection, Proposals,
Subcategory
BibRef
Rezatofighi, S.H.[S. Hamid],
Vijay Kumar, B.G.,
Milan, A.[Anton],
Abbasnejad, M.E.[M. Ehsan],
Dick, A.[Anthony],
Reid, I.D.[Ian D.],
DeepSetNet: Predicting Sets with Deep Neural Networks,
ICCV17(5257-5266)
IEEE DOI
1802
image classification,
learning (artificial intelligence), neural nets,
Random variables
BibRef
Gadde, R.[Raghudeep],
Jampani, V.[Varun],
Gehler, P.V.[Peter V.],
Semantic Video CNNs Through Representation Warping,
ICCV17(4463-4472)
IEEE DOI
1802
CNN for semantic segmentation into CNN for video data.
image sequences, video signal processing, video streaming,
Transforms
BibRef
Kim, D.,
Cho, D.,
Yoo, D.,
Two-Phase Learning for Weakly Supervised Object Localization,
ICCV17(3554-3563)
IEEE DOI
1802
convolution, image annotation, image segmentation,
inference mechanisms, learning (artificial intelligence),
Training
BibRef
Zhou, H.,
Li, Z.,
Ning, C.,
Tang, J.,
CAD: Scale Invariant Framework for Real-Time Object Detection,
AutoRob17(760-768)
IEEE DOI
1802
Convolution, Correlation, Detectors, Feature extraction,
Object detection, Real-time systems
BibRef
Zhang, R.[Rui],
Tang, S.[Sheng],
Zhang, Y.D.[Yong-Dong],
Li, J.T.[Jin-Tao],
Yan, S.C.[Shui-Cheng],
Scale-Adaptive Convolutions for Scene Parsing,
ICCV17(2050-2058)
IEEE DOI
1802
Scale adaptive to get both large and small regions.
regression analysis,
convolutional parameters, convolutional patches,
Training
BibRef
Xu, H.,
Dong, M.,
Zhong, Z.,
Directionally Convolutional Networks for 3D Shape Segmentation,
ICCV17(2717-2726)
IEEE DOI
1802
convolution, image representation, image segmentation,
learning (artificial intelligence), shape recognition,
BibRef
Chen, X.,
Zheng, A.,
Li, J.,
Lu, F.,
Look, Perceive and Segment: Finding the Salient Objects in Images via
Two-stream Fixation-Semantic CNNs,
ICCV17(1050-1058)
IEEE DOI
1802
feature extraction, image segmentation,
learning (artificial intelligence), object detection, SOD,
Streaming media
BibRef
He, S.,
Jiao, J.,
Zhang, X.,
Han, G.,
Lau, R.W.H.,
Delving into Salient Object Subitizing and Detection,
ICCV17(1059-1067)
IEEE DOI
1802
backpropagation, image representation,
neural nets, object detection, adaptive weight layer,
Training
BibRef
Liu, Y.,
Li, H.,
Yan, J.,
Wei, F.,
Wang, X.,
Tang, X.,
Recurrent Scale Approximation for Object Detection in CNN,
ICCV17(571-579)
IEEE DOI
1802
convolution, feature extraction, neural nets, object detection, RSA,
convolutional neural network, feature maps,
Prediction algorithms
BibRef
Moniruzzaman, M.[Mohammed],
Islam, S.M.S.[Syed Mohammed Shamsul],
Bennamoun, M.[Mohammed],
Lavery, P.[Paul],
Deep Learning on Underwater Marine Object Detection: A Survey,
ACIVS17(150-160).
Springer DOI
1712
BibRef
Bianco, S.[Simone],
Buzzelli, M.[Marco],
Schettini, R.[Raimondo],
A Fully Convolutional Network for Salient Object Detection,
CIAP17(II:82-92).
Springer DOI
1711
BibRef
Li, Q.Q.[Quan-Quan],
Jin, S.Y.[Sheng-Ying],
Yan, J.J.[Jun-Jie],
Mimicking Very Efficient Network for Object Detection,
CVPR17(7341-7349)
IEEE DOI
1711
Acceleration, Detectors, Feature extraction, Object detection,
Proposals, Training
BibRef
Kong, X.Y.[Xiang-Yu],
Xin, B.[Bo],
Wang, Y.Z.[Yi-Zhou],
Hua, G.[Gang],
Collaborative Deep Reinforcement Learning for Joint Object Search,
CVPR17(7072-7081)
IEEE DOI
1711
Bicycles, Collaboration, Learning (artificial intelligence),
Logic gates, Object detection, Proposals, Search, problems
BibRef
Jetley, S.[Saumya],
Sapienza, M.[Michael],
Golodetz, S.[Stuart],
Torr, P.H.S.[Philip H.S.],
Straight to Shapes: Real-Time Detection of Encoded Shapes,
CVPR17(4207-4216)
IEEE DOI
1711
Encoding, Object detection, Pipelines, Proposals, Real-time systems,
Shape, Training
BibRef
Zhang, X.L.[Xiao-Lin],
Wei, Y.C.[Yun-Chao],
Kang, G.L.[Guo-Liang],
Yang, Y.[Yi],
Huang, T.S.[Thomas S.],
Self-produced Guidance for Weakly-Supervised Object Localization,
ECCV18(XII: 610-625).
Springer DOI
1810
BibRef
Jie, Z.Q.[Ze-Qun],
Wei, Y.C.[Yun-Chao],
Jin, X.J.[Xiao-Jie],
Feng, J.S.[Jia-Shi],
Liu, W.[Wei],
Deep Self-Taught Learning for Weakly Supervised Object Localization,
CVPR17(4294-4302)
IEEE DOI
1711
Correlation, Detectors, Feature extraction, Proposals, Reliability,
Support vector machines, Training
BibRef
Wu, B.,
Iandola, F.,
Jin, P.H.,
Keutzer, K.,
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural
Networks for Real-Time Object Detection for Autonomous Driving,
ECVW17(446-454)
IEEE DOI
1709
Computational modeling, Detectors, Feature extraction,
Object detection, Pipelines, Proposals, Real-time, systems
BibRef
Kudo, Y.[Yasunori],
Aoki, Y.[Yoshimitsu],
Dilated convolutions for image classification and object localization,
MVA17(452-455)
DOI Link
1708
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
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, 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
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, DNN .