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
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
Li, Y.S.[Yan-Sheng],
Zhang, Y.J.[Yong-Jun],
Huang, X.[Xin],
Yuille, A.L.[Alan L.],
Deep networks under scene-level supervision for multi-class
geospatial object detection from remote sensing images,
PandRS(146), 2018, pp. 182-196.
Elsevier DOI
1812
Multi-class geospatial object detection, Deep networks,
Scene-level supervision,
Class-specific activation weights
BibRef
Li, Y.S.[Yan-Sheng],
Zhang, Y.J.[Yong-Jun],
Zhu, Z.H.[Zhi-Hui],
Error-Tolerant Deep Learning for Remote Sensing Image Scene
Classification,
Cyber(51), No. 4, April 2021, pp. 1756-1768.
IEEE DOI
2103
Remote sensing, Noise measurement, Machine learning, Robustness,
Computer vision, Collaboration, Error correction,
RSSC-oriented error-tolerant deep learning (RSSC-ETDL)
BibRef
Sangineto, E.[Enver],
Nabi, M.[Moin],
Culibrk, D.[Dubravko],
Sebe, N.[Nicu],
Self Paced Deep Learning for Weakly Supervised Object Detection,
PAMI(41), No. 3, March 2019, pp. 712-725.
IEEE DOI
1902
Training, Protocols, Object detection, Reliability, Task analysis,
Machine learning, Detectors, Weakly supervised learning,
training protocol
BibRef
Soviany, P.[Petru],
Ionescu, R.T.[Radu Tudor],
Rota, P.[Paolo],
Sebe, N.[Nicu],
Curriculum self-paced learning for cross-domain object detection,
CVIU(204), 2021, pp. 103166.
Elsevier DOI
2102
Object detection, Cross-domain, Unsupervised domain adaptation,
Curriculum learning, Self-paced learning
BibRef
Zhuo, X.Y.[Xiang-Yu],
Fraundorfer, F.[Friedrich],
Kurz, F.[Franz],
Reinartz, P.[Peter],
Automatic Annotation of Airborne Images by Label Propagation Based on
a Bayesian-CRF Model,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link
1902
Annotation for deep learning input.
BibRef
Zhu, J.[Jun],
Zhu, J.C.[Jiang-Cheng],
Wan, X.D.[Xu-Dong],
Wu, C.[Chao],
Xu, C.[Chao],
Object detection and localization in 3D environment by fusing raw
fisheye image and attitude data,
JVCIR(59), 2019, pp. 128-139.
Elsevier DOI
1903
Object detection, Deep learning, Data fusion, Fisheye camera,
Micro aerial vehicle, Localization
BibRef
Siméoni, O.[Oriane],
Iscen, A.[Ahmet],
Tolias, G.[Giorgos],
Avrithis, Y.[Yannis],
Chum, O.[Ondrej],
Graph-based particular object discovery,
MVA(30), No. 2, March 2019, pp. 243-254.
Springer DOI
1904
BibRef
Earlier:
Unsupervised Object Discovery for Instance Recognition,
WACV18(1745-1754)
IEEE DOI
1806
With background clutter.
computer vision, feedforward neural nets, graph theory,
image representation, image retrieval, object detection,
BibRef
Shen, Z.Y.[Zong-Ying],
Han, S.Y.[Shiang-Yu],
Fu, L.C.[Li-Chen],
Hsiao, P.Y.[Pei-Yung],
Lau, Y.C.[Yo-Chung],
Chang, S.J.[Sheng-Jen],
Deep convolution neural network with scene-centric and object-centric
information for object detection,
IVC(85), 2019, pp. 14-25.
Elsevier DOI
1905
Deep learning, Convolutional neural networks,
Real-time object detection, Scene information
BibRef
Xie, W.Y.[Wei-Ying],
Qin, H.[Haonan],
Li, Y.S.[Yun-Song],
Wang, Z.[Zhuo],
Lei, J.[Jie],
A Novel Effectively Optimized One-Stage Network for Object Detection
in Remote Sensing Imagery,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link
1906
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.[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
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
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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
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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],
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ASAN: Self-Attending and Semantic Activating Network towards Better
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IEICE(E103-D), No. 3, March 2020, pp. 648-659.
WWW Link.
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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],
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Image saliency detection via multi-scale iterative CNN,
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Wu, M.H.[Ming-Hu],
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Wang, J.[Juan],
Huang, Y.X.[Yong-Xi],
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Jiang, Y.[Yuhan],
Ke, C.[Cong],
Zeng, C.[Cheng],
Object detection based on RGC mask R-CNN,
IET-IPR(14), No. 8, 19 June 2020, pp. 1502-1508.
DOI Link
2005
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Yuan, J.[Jin],
Xiong, H.C.[Heng-Chang],
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Guan, W.[Weili],
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Hong, R.[Richang],
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Gated CNN: Integrating multi-scale feature layers for object
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PR(105), 2020, pp. 107131.
Elsevier DOI
2006
Gated CNN, object detection, multi-scale feature layers, explainable CNN
BibRef
Zhou, C.,
Yuan, J.,
Occlusion Pattern Discovery for Object Detection and Occlusion
Reasoning,
CirSysVideo(30), No. 7, July 2020, pp. 2067-2080.
IEEE DOI
2007
Cognition, Object detection, Deformable models, Mixture models,
Detectors, Pattern matching, Adaptation models,
Faster R-CNN
BibRef
Xie, S.R.[Shao-Rong],
Liu, C.[Chang],
Gao, J.T.[Jian-Tao],
Li, X.M.[Xiao-Mao],
Luo, J.[Jun],
Fan, B.[Baojie],
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Pu, H.Y.[Hua-Yan],
Peng, Y.[Yan],
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JVCIR(70), 2020, pp. 102770.
Elsevier DOI
2007
Object detection, Convolutional neural network,
Context aggregation, Multi-scale contextual representations
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He, X.,
Bai, S.,
Chu, J.,
Bai, X.,
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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
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Zhang, Q.[Qing],
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PR(107), 2020, pp. 107484.
Elsevier DOI
2008
Salient object detection, Visual saliency, Feature learning,
Fully convolutional neural network
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Xu, D.L.[Dong-Li],
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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,
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Chen, C.,
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RRNet:
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VisDrone19(100-108)
IEEE DOI
2004
image capture, learning (artificial intelligence), neural nets,
object detection, regression analysis, Deep Learning
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Azadi, S.[Samaneh],
Pathak, D.[Deepak],
Ebrahimi, S.[Sayna],
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Springer DOI
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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
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Liu, C.,
Zhou, W.,
Chen, Y.,
Lei, J.,
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IEEE DOI
2010
Feature extraction, Decoding, Convolution, Adaptation models, Fuses,
Visualization, Object detection, RGB-D,
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BibRef
Zhang, R.Q.[Rui-Qian],
Shao, Z.F.[Zhen-Feng],
Huang, X.[Xiao],
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Object Detection in UAV Images via Global Density Fused Convolutional
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Chen, S.,
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Relation R-CNN:
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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],
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Deng, S.T.[Su-Tao],
Li, S.A.[Shu-Ai],
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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,
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BibRef
Zhang, S.,
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RefineDet++: Single-Shot Refinement Neural Network for Object
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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
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Zhang, S.,
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Bian, X.,
Lei, Z.,
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Single-Shot Refinement Neural Network for Object Detection,
CVPR18(4203-4212)
IEEE DOI
1812
Object detection, Detectors, Feature extraction, Task analysis,
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Hassanzadeh, T.,
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2D to 3D Evolutionary Deep Convolutional Neural Networks for Medical
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IEEE DOI
2102
Biomedical imaging, Image segmentation, Encoding,
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Sun, X.[Xian],
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Liu, Y.F.[Ying-Fei],
Fu, K.[Kun],
PBNet: Part-based convolutional neural network for complex composite
object detection in remote sensing imagery,
PandRS(173), 2021, pp. 50-65.
Elsevier DOI
2102
Object detection, Remote sensing imagery,
Complex composite object, Part-based detection, Context information
BibRef
Seo, H.,
Bassenne, M.,
Xing, L.,
Closing the Gap Between Deep Neural Network Modeling and Biomedical
Decision-Making Metrics in Segmentation via Adaptive Loss Functions,
MedImg(40), No. 2, February 2021, pp. 585-593.
IEEE DOI
2102
Training, Neural networks, Measurement, Adaptation models,
Decision making, Deep learning, Harmonic analysis, Deep learning,
Segmentation
BibRef
Chen, X.,
Li, H.,
Wu, Q.,
Ngan, K.N.,
Xu, L.,
High-Quality R-CNN Object Detection Using Multi-Path Detection
Calibration Network,
CirSysVideo(31), No. 2, February 2021, pp. 715-727.
IEEE DOI
2102
Proposals, Detectors, Feature extraction, Calibration,
Object detection, Benchmark testing, Neural networks,
object recognition
BibRef
Song, L.Y.[Ling-Yun],
Liu, J.[Jun],
Sun, M.X.[Ming-Xuan],
Shang, X.Q.[Xue-Qun],
Weakly Supervised Group Mask Network for Object Detection,
IJCV(129), No. 3, March 2021, pp. 681-702.
Springer DOI
2103
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.[Nuno],
Cascade R-CNN: High Quality Object Detection and Instance
Segmentation,
PAMI(43), No. 5, May 2021, pp. 1483-1498.
IEEE DOI
2104
BibRef
Earlier:
Cascade R-CNN: Delving Into High Quality Object Detection,
CVPR18(6154-6162)
IEEE DOI
1812
Detectors, Object detection, Training, Proposals, Task analysis,
Computer architecture, Feature extraction, Object detection,
instance segmentation.
Noise measurement
BibRef
Fang, W.H.[Wen-Hao],
Han, X.H.[Xian-Hua],
Spatial and Channel Attention Modulated Network for Medical Image
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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
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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
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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
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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:
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ECCV20(XXI:481-496).
Springer DOI
2011
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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
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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
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ECCV20(II:35-51).
Springer DOI
2011
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Amrouch, M.[Mustapha],
Convolutional Neural Networks Backbones for Object Detection,
ICISP20(282-289).
Springer DOI
2009
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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
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Liu, J.,
Hou, Q.,
Cheng, M.,
Wang, C.,
Feng, J.,
Improving Convolutional Networks With Self-Calibrated Convolutions,
CVPR20(10093-10102)
IEEE DOI
2008
Convolutional codes, Computer architecture, Task analysis, Kernel,
Calibration, Standards, Object detection
BibRef
Singh, S.,
Krishnan, S.,
Filter Response Normalization Layer: Eliminating Batch Dependence in
the Training of Deep Neural Networks,
CVPR20(11234-11243)
IEEE DOI
2008
Training, Degradation, Computer architecture, Neural networks,
Object detection, Task analysis, Testing
BibRef
Cai, Q.,
Pan, Y.,
Wang, Y.,
Liu, J.,
Yao, T.,
Mei, T.,
Learning a Unified Sample Weighting Network for Object Detection,
CVPR20(14161-14170)
IEEE DOI
2008
Detectors, Task analysis, Object detection, Training, Uncertainty,
Proposals, Training data
BibRef
Joung, S.,
Kim, S.,
Kim, H.,
Kim, M.,
Kim, I.,
Cho, J.,
Sohn, K.,
Cylindrical Convolutional Networks for Joint Object Detection and
Viewpoint Estimation,
CVPR20(14151-14160)
IEEE DOI
2008
Feature extraction, Estimation, Kernel,
Object recognition, Object detection
BibRef
Guo, C.,
Fan, B.,
Zhang, Q.,
Xiang, S.,
Pan, C.,
AugFPN: Improving Multi-Scale Feature Learning for Object Detection,
CVPR20(12592-12601)
IEEE DOI
2008
Feature extraction, Detectors, Semantics, Object detection,
Proposals, Convolution, Data mining
BibRef
Wang, A.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
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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
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Farhadi, M.[Mohammad],
Yang, Y.Z.[Ye-Zhou],
TKD: Temporal Knowledge Distillation for Active Perception,
WACV20(942-951)
IEEE DOI
2006
Code, Object Detection.
WWW Link. Temporal knowledge over NN applied over multiple frames.
Adaptation models, Object detection, Visualization,
Computational modeling, Task analysis, Training, Feature extraction
BibRef
Li, Y.,
Chen, Y.,
Wang, N.,
Zhang, Z.,
Scale-Aware Trident Networks for Object Detection,
ICCV19(6053-6062)
IEEE DOI
2004
Code, Object Detection.
WWW Link. feature extraction,
learning (artificial intelligence), neural nets,
Computer architecture
BibRef
Li, P.,
Chen, B.,
Ouyang, W.,
Wang, D.,
Yang, X.,
Lu, H.,
GradNet: Gradient-Guided Network for Visual Object Tracking,
ICCV19(6161-6170)
IEEE DOI
2004
convolutional neural nets, feature extraction, gradient methods,
object detection, object tracking, visual object tracking,
Real-time systems
BibRef
Xu, H.,
Yao, L.,
Li, Z.,
Liang, X.,
Zhang, W.,
Auto-FPN: Automatic Network Architecture Adaptation for Object
Detection Beyond Classification,
ICCV19(6648-6657)
IEEE DOI
2004
gradient methods, image classification,
object detection, NAS, CNN architecture, image classification, Training
BibRef
Yang, K.,
Li, D.,
Dou, Y.,
Towards Precise End-to-End Weakly Supervised Object Detection Network,
ICCV19(8371-8380)
IEEE DOI
2004
learning (artificial intelligence), object detection,
regression analysis, object position prediction, local minima,
BibRef
Peng, J.R.[Jun-Ran],
Sun, M.[Ming],
Zhang, Z.X.[Zhao-Xiang],
Tan, T.N.[Tie-Niu],
Yan, J.J.[Jun-Jie],
POD: Practical Object Detection With Scale-Sensitive Network,
ICCV19(9606-9615)
IEEE DOI
2004
image filtering, learning (artificial intelligence),
object detection, optimisation, practical object detection,
Optimization
BibRef
Tian, Z.,
Shen, C.,
Chen, H.,
He, T.,
FCOS: Fully Convolutional One-Stage Object Detection,
ICCV19(9626-9635)
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
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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
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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
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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
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Dusmanu, M.[Mihai],
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Pollefeys, M.[Marc],
Multi-view Optimization of Local Feature Geometry,
ECCV20(I:670-686).
Springer DOI
2011
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Dusmanu, M.[Mihai],
Rocco, I.[Ignacio],
Pajdla, T.[Tomas],
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Sattler, T.[Torsten],
D2-Net: A Trainable CNN for Joint Description and Detection of Local
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CVPR19(8084-8093).
IEEE DOI
2002
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Zhu, R.[Rui],
Zhang, S.F.[Shi-Feng],
Wang, X.B.[Xiao-Bo],
Wen, L.Y.[Long-Yin],
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ScratchDet: Training Single-Shot Object Detectors From Scratch,
CVPR19(2263-2272).
IEEE DOI
2002
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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,
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IEEE DOI
2002
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Pang, J.M.[Jiang-Miao],
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Libra R-CNN: Towards Balanced Learning for Object Detection,
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IEEE DOI
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Voigtlaender, P.[Paul],
Chai, Y.N.[Yu-Ning],
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Adam, H.[Hartwig],
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FEELVOS: Fast End-To-End Embedding Learning for Video Object
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IEEE DOI
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Wang, H.Y.[Hui-Yu],
Zhu, Y.[Yukun],
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Axial-Deeplab: Stand-alone Axial-Attention for Panoptic Segmentation,
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2011
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Object Detection With Location-Aware Deformable Convolution and
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IEEE DOI
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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:
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CVPR19(9193-9202).
IEEE DOI
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Carrilho, A.C.,
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Automatic Object Extraction From High Resolution Aerial Imagery With
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ICIP19(2194-2198)
IEEE DOI
1910
Object detection, Infrared data fusion, Attention module
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Ghosh, S.,
Srinivasa, S.K.K.,
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Deep Network Pruning for Object Detection,
ICIP19(3915-3919)
IEEE DOI
1910
Object Detection, Deep Learning, CNN, Network Pruning, Clustering
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Zhang, C.,
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Salient Object Detection via Deep Hierarchical Context Aggregation
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ICIP19(2941-2945)
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1910
saliency detection, deep layer aggregation, intermediate supervision
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Naiden, A.,
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Shift R-CNN: Deep Monocular 3D Object Detection With Closed-Form
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ICIP19(61-65)
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1910
Monocular 3D object detection, convolutional neural networks,
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1909
Low object to image ratio. Lots of small objects in a very large image.
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1909
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Unsupervised Adversarial Visual Level Domain Adaptation for Learning
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WACV19(1807-1815)
IEEE DOI
1904
image annotation, object detection, unsupervised learning,
video signal processing, unannotated video dataset,
Image color analysis
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Ebrahimpour, M.K.,
Li, J.,
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WACV19(986-994)
IEEE DOI
1904
brain, convolutional neural nets, feature extraction,
image classification, neurophysiology, object detection,
Training
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Chen, Y.,
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1812
Object detection, Training, Adaptation models, Proposals,
Task analysis, Lighting, Feature extraction
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Zhang, Y.,
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He, Z.,
Lee, H.,
Unsupervised Discovery of Object Landmarks as Structural
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CVPR18(2694-2703)
IEEE DOI
1812
Visualization, Neural networks, Decoding, Image reconstruction,
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Uijlings, J.R.R.,
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Revisiting Knowledge Transfer for Training Object Class Detectors,
CVPR18(1101-1110)
IEEE DOI
1812
Proposals, Detectors, Training, Knowledge transfer, Semantics,
Neural networks, Standards
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Pointwise Convolutional Neural Networks,
CVPR18(984-993)
IEEE DOI
1812
Convolution, Semantics, Kernel, Shape, Object recognition, Task analysis
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Li, Y.,
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CSRNet: Dilated Convolutional Neural Networks for Understanding the
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CVPR18(1091-1100)
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1812
Feature extraction, Convolution, Kernel, Task analysis, Training,
Image analysis, Pattern recognition
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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
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Womg, A.,
Shafiee, M.J.,
Li, F.,
Chwyl, B.,
Tiny SSD: A Tiny Single-Shot Detection Deep Convolutional Neural
Network for Real-Time Embedded Object Detection,
CRV18(95-101)
IEEE DOI
1812
Object detection, Fires, Microarchitecture, Network architecture,
Real-time systems, Neural networks, Feature extraction,
single-shot
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Toudeshki, A.G.,
Shamshirdar, F.,
Vaughan, R.,
Robust UAV Visual Teach and Repeat Using Only Sparse Semantic Object
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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
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Wang, P.[Peng],
Yuille, A.L.[Alan L.],
DOC: Deep OCclusion Estimation from a Single Image,
ECCV16(I: 545-561).
Springer DOI
1611
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Tang, Z.Q.[Zhi-Qiang],
Peng, X.[Xi],
Geng, S.J.[Shi-Jie],
Wu, L.F.[Ling-Fei],
Zhang, S.T.[Shao-Ting],
Metaxas, D.[Dimitris],
Quantized Densely Connected U-Nets for Efficient Landmark Localization,
ECCV18(III: 348-364).
Springer DOI
1810
BibRef
Wu, Z.X.[Zu-Xuan],
Han, X.T.[Xin-Tong],
Lin, Y.L.[Yen-Liang],
Uzunbas, M.G.[Mustafa Gökhan],
Goldstein, T.[Tom],
Lim, S.N.[Ser Nam],
Davis, L.S.[Larry S.],
DCAN: Dual Channel-Wise Alignment Networks for Unsupervised Scene
Adaptation,
ECCV18(VI: 535-552).
Springer DOI
1810
pixel annotations to train NN for segmentation.
BibRef
Wei, Y.[Yi],
Pan, X.Y.[Xin-Yu],
Qin, H.W.[Hong-Wei],
Ouyang, W.L.[Wan-Li],
Yan, J.J.[Jun-Jie],
Quantization Mimic: Towards Very Tiny CNN for Object Detection,
ECCV18(VIII: 274-290).
Springer DOI
1810
BibRef
Liu, S.T.[Song-Tao],
Huang, D.[Di],
Wang, Y.H.[Yun-Hong],
Receptive Field Block Net for Accurate and Fast Object Detection,
ECCV18(XI: 404-419).
Springer DOI
1810
BibRef
Gu, J.Y.[Jia-Yuan],
Hu, H.[Han],
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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
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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, Computer architecture, Data models, 3D Object Recognition
BibRef
Zhang, X.,
Chen, Y.,
Zhu, B.,
Wang, J.,
Tang, M.,
Lu, H.,
Tree Hierarchical CNNs for Object Parsing,
ICIP18(1588-1592)
IEEE DOI
1809
Image segmentation, Head, Semantics, Torso, Visualization,
Legged locomotion, Cows, object parsing, tree hierarchical CNNs,
part-aware fusion
BibRef
Fukagai, T.,
Maeda, K.,
Tanabe, S.,
Shirahata, K.,
Tomita, Y.,
Ike, A.,
Nakagawa, A.,
Speed-Up of Object Detection Neural Network with GPU,
ICIP18(301-305)
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
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, M.E.[M. Ehsan],
Dick, A.[Anthony],
Reid, I.D.[Ian D.],
DeepSetNet: Predicting Sets with Deep Neural Networks,
ICCV17(5257-5266)
IEEE DOI
1802
image classification,
learning (artificial intelligence), neural nets,
Random variables
BibRef
Gadde, R.[Raghudeep],
Jampani, V.[Varun],
Gehler, P.V.[Peter V.],
Semantic Video CNNs Through Representation Warping,
ICCV17(4463-4472)
IEEE DOI
1802
CNN for semantic segmentation into CNN for video data.
image sequences, video signal processing, video streaming,
Transforms
BibRef
Kim, D.,
Cho, D.,
Yoo, D.,
Two-Phase Learning for Weakly Supervised Object Localization,
ICCV17(3554-3563)
IEEE DOI
1802
convolution, image annotation, image segmentation,
inference mechanisms, learning (artificial intelligence),
Training
BibRef
Zhu, Y.,
Zhao, C.,
Wang, J.,
Zhao, X.,
Wu, Y.,
Lu, H.,
CoupleNet:
Coupling Global Structure with Local Parts for Object Detection,
ICCV17(4146-4154)
IEEE DOI
1802
convolution, image classification, neural nets, object detection,
Convolutional Neural Network detectors, CoupleNet,
Visualization
BibRef
Dvornik, N.[Nikita],
Mairal, J.[Julien],
Schmid, C.[Cordelia],
Modeling Visual Context Is Key to Augmenting Object Detection Datasets,
ECCV18(XII: 375-391).
Springer DOI
1810
BibRef
Dvornik, N.[Nikita],
Shmelkov, K.,
Mairal, J.[Julien],
Schmid, C.[Cordelia],
BlitzNet: A Real-Time Deep Network for Scene Understanding,
ICCV17(4174-4182)
IEEE DOI
1802
image segmentation,
learning (artificial intelligence), object detection,
Semantics
BibRef
Zhou, H.,
Li, Z.,
Ning, C.,
Tang, J.,
CAD: Scale Invariant Framework for Real-Time Object Detection,
AutoRob17(760-768)
IEEE DOI
1802
Convolution, Correlation, Detectors, Feature extraction,
Object detection, Real-time systems
BibRef
Zhang, R.[Rui],
Tang, S.[Sheng],
Zhang, Y.D.[Yong-Dong],
Li, J.T.[Jin-Tao],
Yan, S.C.[Shui-Cheng],
Scale-Adaptive Convolutions for Scene Parsing,
ICCV17(2050-2058)
IEEE DOI
1802
Scale adaptive to get both large and small regions.
regression analysis,
convolutional parameters, convolutional patches,
Training
BibRef
Xu, H.,
Dong, M.,
Zhong, Z.,
Directionally Convolutional Networks for 3D Shape Segmentation,
ICCV17(2717-2726)
IEEE DOI
1802
convolution, image representation, image segmentation,
learning (artificial intelligence), shape recognition,
BibRef
Chen, X.,
Zheng, A.,
Li, J.,
Lu, F.,
Look, Perceive and Segment: Finding the Salient Objects in Images via
Two-stream Fixation-Semantic CNNs,
ICCV17(1050-1058)
IEEE DOI
1802
feature extraction, image segmentation,
learning (artificial intelligence), object detection, SOD,
Streaming media
BibRef
He, S.,
Jiao, J.,
Zhang, X.,
Han, G.,
Lau, R.W.H.,
Delving into Salient Object Subitizing and Detection,
ICCV17(1059-1067)
IEEE DOI
1802
backpropagation, image representation,
neural nets, object detection, adaptive weight layer,
Training
BibRef
Liu, Y.,
Li, H.,
Yan, J.,
Wei, F.,
Wang, X.,
Tang, X.,
Recurrent Scale Approximation for Object Detection in CNN,
ICCV17(571-579)
IEEE DOI
1802
convolution, feature extraction, neural nets, object detection, RSA,
convolutional neural network, feature maps,
Prediction algorithms
BibRef
Moniruzzaman, M.[Mohammed],
Islam, S.M.S.[Syed Mohammed Shamsul],
Bennamoun, M.[Mohammed],
Lavery, P.[Paul],
Deep Learning on Underwater Marine Object Detection: A Survey,
ACIVS17(150-160).
Springer DOI
1712
BibRef
Bianco, S.[Simone],
Buzzelli, M.[Marco],
Schettini, R.[Raimondo],
A Fully Convolutional Network for Salient Object Detection,
CIAP17(II:82-92).
Springer DOI
1711
BibRef
Li, Q.Q.[Quan-Quan],
Jin, S.Y.[Sheng-Ying],
Yan, J.J.[Jun-Jie],
Mimicking Very Efficient Network for Object Detection,
CVPR17(7341-7349)
IEEE DOI
1711
Acceleration, Detectors, Feature extraction, Object detection,
Proposals, Training
BibRef
Kong, X.Y.[Xiang-Yu],
Xin, B.[Bo],
Wang, Y.Z.[Yi-Zhou],
Hua, G.[Gang],
Collaborative Deep Reinforcement Learning for Joint Object Search,
CVPR17(7072-7081)
IEEE DOI
1711
Bicycles, Collaboration, Learning (artificial intelligence),
Logic gates, Object detection, Proposals, Search, problems
BibRef
Jetley, S.[Saumya],
Sapienza, M.[Michael],
Golodetz, S.[Stuart],
Torr, P.H.S.[Philip H.S.],
Straight to Shapes: Real-Time Detection of Encoded Shapes,
CVPR17(4207-4216)
IEEE DOI
1711
Encoding, Object detection, Pipelines, Proposals, Real-time systems,
Shape, Training
BibRef
Zhang, X.L.[Xiao-Lin],
Wei, Y.C.[Yun-Chao],
Kang, G.L.[Guo-Liang],
Yang, Y.[Yi],
Huang, T.S.[Thomas S.],
Self-produced Guidance for Weakly-Supervised Object Localization,
ECCV18(XII: 610-625).
Springer DOI
1810
BibRef
Jie, Z.Q.[Ze-Qun],
Wei, Y.C.[Yun-Chao],
Jin, X.J.[Xiao-Jie],
Feng, J.S.[Jia-Shi],
Liu, W.[Wei],
Deep Self-Taught Learning for Weakly Supervised Object Localization,
CVPR17(4294-4302)
IEEE DOI
1711
Correlation, Detectors, Feature extraction, Proposals, Reliability,
Support vector machines, Training
BibRef
Wu, B.,
Iandola, F.,
Jin, P.H.,
Keutzer, K.,
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural
Networks for Real-Time Object Detection for Autonomous Driving,
ECVW17(446-454)
IEEE DOI
1709
Computational modeling, Detectors, Feature extraction,
Object detection, Pipelines, Proposals, Real-time, systems
BibRef
Kudo, Y.[Yasunori],
Aoki, Y.[Yoshimitsu],
Dilated convolutions for image classification and object localization,
MVA17(452-455)
DOI Link
1708
Computer vision, Error analysis, Image recognition,
Image resolution, Image segmentation, Organizations, Pattern recognition
BibRef
Roh, M.C.,
Lee, J.Y.,
Refining faster-RCNN for accurate object detection,
MVA17(514-517)
DOI Link
1708
Art, Detectors, Licenses, Object detection, Organizations, Proposals, Training
BibRef
Brahmbhatt, S.[Samarth],
Christensen, H.I.[Henrik I.],
Hays, J.H.[James H.],
StuffNet: Using 'Stuff' to Improve Object Detection,
WACV17(934-943)
IEEE DOI
1609
Context, Feature extraction, Image segmentation, Object detection,
Proposals, Semantics, Training
BibRef
Xiang, Y.,
Choi, W.,
Lin, Y.,
Savarese, S.,
Subcategory-Aware Convolutional Neural Networks for Object Proposals
and Detection,
WACV17(924-933)
IEEE DOI
1609
Feature extraction, Heating systems, Object detection, Proposals,
Training.
BibRef
Siam, M.,
Valipour, S.,
Jagersand, M.,
Ray, N.,
Convolutional gated recurrent networks for video segmentation,
ICIP17(3090-3094)
IEEE DOI
1803
BibRef
Earlier: A2, A1, A3, A4:
Recurrent Fully Convolutional Networks for Video Segmentation,
WACV17(29-36)
IEEE DOI
1609
Computer architecture, Deconvolution, Image segmentation,
Logic gates, Motion segmentation, Semantics, Training,
Video Semantic Segmentation.
Neural networks.
BibRef
Zhang, J.,
Dai, Y.,
Li, B.,
He, M.,
Attention to the Scale: Deep Multi-Scale Salient Object Detection,
DICTA17(1-7)
IEEE DOI
1804
feature extraction, image fusion,
learning (artificial intelligence), neural nets,
Semantics
BibRef
Zhang, J.,
Dai, Y.,
Porikli, F.M.[Fatih M.],
Deep Salient Object Detection by Integrating Multi-level Cues,
WACV17(1-10)
IEEE DOI
1609
Feature extraction, Image resolution, Image segmentation,
Machine learning, Neural networks, Object detection, Semantics
BibRef
Guo, S.X.[Shu-Xuan],
Liu, L.[Li],
Wang, W.[Wei],
Lao, S.Y.[Song-Yang],
Wang, L.[Liang],
An attention model based on spatial transformers for scene
recognition,
ICPR16(3757-3762)
IEEE DOI
1705
Databases, Feature extraction, Image recognition, Modeling,
Pattern recognition, Pipelines, Visualization
BibRef
Toca, C.,
Patrascu, C.,
Ciuc, M.,
AutoMarkov DNNs for object classification,
ICPR16(3452-3457)
IEEE DOI
1705
Biological neural networks, Computer architecture, Convolution,
Convolutional codes, Markov random fields, Neurons, Testing
BibRef
Sun, M.,
Han, T.X.,
Liu, M.C.[Ming-Chang],
Khodayari-Rostamabad, A.,
Multiple Instance Learning Convolutional Neural Networks for object
recognition,
ICPR16(3270-3275)
IEEE DOI
1705
Benchmark testing, Machine learning, Neural networks,
Object recognition, Optimization, Prediction algorithms, Training
BibRef
Shimoda, W.,
Yanai, K.,
Weakly-supervised segmentation by combining CNN feature maps and
object saliency maps,
ICPR16(1935-1940)
IEEE DOI
1705
Adaptation models, Convolution, Feature extraction,
Image segmentation, Proposals, Semantics, Training
BibRef
Chen, C.[Chenyi],
Liu, M.Y.[Ming-Yu],
Tuzel, O.[Oncel],
Xiao, J.X.[Jian-Xiong],
R-CNN for Small Object Detection,
ACCV16(V: 214-230).
Springer DOI
1704
BibRef
Wang, Y.[Yida],
Cui, C.[Can],
Zhou, X.Z.[Xiu-Zhuang],
Deng, W.H.[Wei-Hong],
ZigzagNet:
Efficient Deep Learning for Real Object Recognition Based on 3D Models,
ACCV16(IV: 456-471).
Springer DOI
1704
BibRef
Nishida, K.[Kenshiro],
Hotta, K.[Kazuhiro],
Particle Detection in Crowd Regions Using Cumulative Score of CNN,
ISVC16(II: 566-575).
Springer DOI
1701
BibRef
Wang, C.,
Siddiqi, K.,
Differential Geometry Boosts Convolutional Neural Networks for Object
Detection,
DIFF-CV16(1006-1013)
IEEE DOI
1612
BibRef
Tran, D.,
Wang, H.,
Feiszli, M.,
Torresani, L.,
Video Classification With Channel-Separated Convolutional Networks,
ICCV19(5551-5560)
IEEE DOI
2004
convolutional neural nets, image classification,
learning (artificial intelligence), neural net architecture,
Computational efficiency
BibRef
Tran, D.[Du],
Bourdev, L.[Lubomir],
Fergus, R.[Rob],
Torresani, L.[Lorenzo],
Paluri, M.[Manohar],
Deep End2End Voxel2Voxel Prediction,
DeepLearn-C16(402-409)
IEEE DOI
1612
BibRef
Yang, B.[Bin],
Yan, J.J.[Jun-Jie],
Lei, Z.[Zhen],
Li, S.Z.[Stan Z.],
CRAFT Objects from Images,
CVPR16(6043-6051)
IEEE DOI
1612
CRAFT: Cascade Regionproposal-network And FasT-rcnn.
BibRef
Li, C.Y.[Chun-Yuan],
Stevens, A.[Andrew],
Chen, C.Y.[Chang-You],
Pu, Y.C.[Yun-Chen],
Gan, Z.[Zhe],
Carin, L.[Lawrence],
Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape
Classification,
CVPR16(5666-5675)
IEEE DOI
1612
2d, 3D shape cues.
BibRef
Qi, C.R.[Charles R.],
Su, H.[Hao],
Nießner, M.[Matthias],
Dai, A.[Angela],
Yan, M.Y.[Meng-Yuan],
Guibas, L.J.[Leonidas J.],
Volumetric and Multi-view CNNs for Object Classification on 3D Data,
CVPR16(5648-5656)
IEEE DOI
1612
BibRef
Kuen, J.[Jason],
Wang, Z.H.[Zhen-Hua],
Wang, G.[Gang],
Recurrent Attentional Networks for Saliency Detection,
CVPR16(3668-3677)
IEEE DOI
1612
BibRef
Mathe, S.,
Pirinen, A.,
Sminchisescu, C.[Cristian],
Reinforcement Learning for Visual Object Detection,
CVPR16(2894-2902)
IEEE DOI
1612
BibRef
Misra, I.[Ishan],
Zitnick, C.L.[C. Lawrence],
Mitchell, M.[Margaret],
Girshick, R.[Ross],
Seeing through the Human Reporting Bias:
Visual Classifiers from Noisy Human-Centric Labels,
CVPR16(2930-2939)
IEEE DOI
1612
BibRef
Najibi, M.,
Rastegari, M.,
Davis, L.S.,
G-CNN: An Iterative Grid Based Object Detector,
CVPR16(2369-2377)
IEEE DOI
1612
BibRef
Borji, A.[Ali],
Izadi, S.[Saeed],
Itti, L.[Laurent],
iLab-20M:
A Large-Scale Controlled Object Dataset to Investigate Deep Learning,
CVPR16(2221-2230)
IEEE DOI
1612
Dataset, Learning.
BibRef
Chen, X.Z.[Xiao-Zhi],
Kundu, K.[Kaustav],
Zhu, Y.,
Ma, H.M.[Hui-Min],
Fidler, S.[Sanja],
Urtasun, R.[Raquel],
3D Object Proposals Using Stereo Imagery for Accurate Object Class
Detection,
PAMI(40), No. 5, May 2018, pp. 1259-1272.
IEEE DOI
1804
Context, Detectors, Laser radar, Object detection, Proposals,
Solid modeling, 3D object detection,
stereo
BibRef
Chen, X.Z.[Xiao-Zhi],
Kundu, K.[Kaustav],
Zhang, Z.Y.[Zi-Yu],
Ma, H.M.[Hui-Min],
Fidler, S.[Sanja],
Urtasun, R.[Raquel],
Monocular 3D Object Detection for Autonomous Driving,
CVPR16(2147-2156)
IEEE DOI
1612
BibRef
Hayder, Z.,
He, X.,
Salzmann, M.,
Learning to Co-Generate Object Proposals with a Deep Structured
Network,
CVPR16(2565-2573)
IEEE DOI
1612
BibRef
Jampani, V.[Varun],
Kiefel, M.[Martin],
Gehler, P.V.[Peter V.],
Learning Sparse High Dimensional Filters:
Image Filtering, Dense CRFs and Bilateral Neural Networks,
CVPR16(4452-4461)
IEEE DOI
1612
BibRef
Jampani, V.[Varun],
Gadde, R.[Raghudeep],
Gehler, P.V.[Peter V.],
Video Propagation Networks,
CVPR17(3154-3164)
IEEE DOI
1711
Computer architecture, Image color analysis, Lattices,
Optimization, Runtime, Semantics, Virtual, private, networks
BibRef
Gadde, R.[Raghudeep],
Jampani, V.[Varun],
Kiefel, M.[Martin],
Kappler, D.[Daniel],
Gehler, P.V.[Peter V.],
Superpixel Convolutional Networks Using Bilateral Inceptions,
ECCV16(I: 597-613).
Springer DOI
1611
BibRef
Liu, W.[Wei],
Anguelov, D.[Dragomir],
Erhan, D.[Dumitru],
Szegedy, C.[Christian],
Reed, S.[Scott],
Fu, C.Y.[Cheng-Yang],
Berg, A.C.[Alexander C.],
SSD: Single Shot MultiBox Detector,
ECCV16(I: 21-37).
Springer DOI
1611
BibRef
Cai, Z.W.[Zhao-Wei],
He, X.,
Sun, J.,
Vasconcelos, N.M.[Nuno M.],
Deep Learning with Low Precision by Half-Wave Gaussian Quantization,
CVPR17(5406-5414)
IEEE DOI
1711
Backpropagation, Biological neural networks, Complexity theory,
Computational modeling, Machine learning, Quantization, (signal)
BibRef
Cai, Z.W.[Zhao-Wei],
Fan, Q.F.[Quan-Fu],
Feris, R.S.[Rogerio S.],
Vasconcelos, N.M.[Nuno M.],
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object
Detection,
ECCV16(IV: 354-370).
Springer DOI
1611
BibRef
Tang, Y.[Youbao],
Wu, X.Q.[Xiang-Qian],
Saliency Detection via Combining Region-Level and Pixel-Level
Predictions with CNNs,
ECCV16(VIII: 809-825).
Springer DOI
1611
BibRef
Krusch, P.,
Bochinski, E.,
Eiselein, V.,
Sikora, T.,
A consistent two-level metric for evaluation of automated abandoned
object detection methods,
ICIP17(4352-4356)
IEEE DOI
1803
BibRef
Earlier: A2, A3, A4, Only:
Training a convolutional neural network for multi-class object
detection using solely virtual world data,
AVSS16(278-285)
IEEE DOI
1611
Cameras, Feature extraction, Lighting, Object detection, Protocols,
Volume measurement, abandoned object detection, evaluation, metric,
video surveillance.
Animals
BibRef
Cervantes, E.,
Yu, L.L.,
Bagdanov, A.D.[Andrew D.],
Masana, M.,
van de Weijer, J.[Joost],
Hierarchical part detection with deep neural networks,
ICIP16(1933-1937)
IEEE DOI
1610
Birds
BibRef
Kuo, W.,
Hariharan, B.,
Malik, J.,
DeepBox: Learning Objectness with Convolutional Networks,
ICCV15(2479-2487)
IEEE DOI
1602
Computer architecture
BibRef
Ma, C.H.[Chih-Hao],
Hsu, C.T.[Chiou-Ting],
Huet, B.[Benoit],
Nonparametric scene parsing with deep convolutional features and
dense alignment,
ICIP15(1915-1919)
IEEE DOI
1512
SIFT flow; deep convolutional network; object window; scene parsing
BibRef
Caicedo, J.C.,
Lazebnik, S.[Svetlana],
Active Object Localization with Deep Reinforcement Learning,
ICCV15(2488-2496)
IEEE DOI
1602
Computational modeling
BibRef
Pepik, B.[Bojan],
Benenson, R.[Rodrigo],
Ritschel, T.[Tobias],
Schiele, B.[Bernt],
What Is Holding Back Convnets for Detection?,
GCPR15(517-528).
Springer DOI
1511
BibRef
Mrowca, D.,
Rohrbach, M.[Marcus],
Hoffman, J.,
Hu, R.,
Saenko, K.,
Darrell, T.J.,
Spatial Semantic Regularisation for Large Scale Object Detection,
ICCV15(2003-2011)
IEEE DOI
1602
Clustering algorithms
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Deep Learning, Deep Nets .