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Uijlings, J.[Jasper],
Geusebroek, J.M.[Jan-Mark],
Salient object detection: From pixels to segments,
IVC(31), No. 1, January 2013, pp. 31-42.
Elsevier DOI
1302
Salient object detection; Object-based visual attention theory;
Proto-objects
BibRef
Yanulevskaya, V.[Victoria],
Uijlings, J.[Jasper],
Sebe, N.[Nicu],
Learning to Group Objects,
CVPR14(3134-3141)
IEEE DOI
1409
Class independent object proposals
BibRef
Jie, Z.Q.[Ze-Qun],
Liang, X.D.[Xiao-Dan],
Feng, J.S.[Jia-Shi],
Lu, W.F.[Wen Feng],
Tay, E.H.F.[Eng Hock Francis],
Yan, S.C.[Shui-Cheng],
Scale-Aware Pixelwise Object Proposal Networks,
IP(25), No. 10, October 2016, pp. 4525-4539.
IEEE DOI
1610
neural nets
BibRef
Hu, P.,
Wang, W.,
Zhang, C.,
Lu, K.,
Detecting Salient Objects via Color and Texture Compactness
Hypotheses,
IP(25), No. 10, October 2016, pp. 4653-4664.
IEEE DOI
1610
image classification
BibRef
Huo, L.[Lina],
Jiao, L.C.[Li-Cheng],
Wang, S.[Shuang],
Yang, S.Y.[Shu-Yuan],
Object-level saliency detection with color attributes,
PR(49), No. 1, 2016, pp. 162-173.
Elsevier DOI
1511
Candidate objectness
BibRef
Kuang, P.J.[Pei-Jiang],
Zhou, Z.H.[Zhi-Heng],
Wu, D.C.[Dong-Cheng],
Improved Edge Boxes with Object Saliency and Location Awards,
IEICE(E99-D), No. 2, February 2016, pp. 488-495.
WWW Link.
1604
BibRef
Deng, H.[He],
Sun, X.P.[Xian-Ping],
Liu, M.[Maili],
Ye, C.H.[Chao-Hui],
Zhou, X.[Xin],
Entropy-based window selection for detecting dim and small infrared
targets,
PR(61), No. 1, 2017, pp. 66-77.
Elsevier DOI
1609
Dim and small target detection
BibRef
Lee, D.[Daeha],
Kim, J.[Jaehong],
Kim, H.H.[Ho-Hee],
Kim, S.J.[Soon-Ja],
The Computation Reduction in Object Detection via Composite Structure
of Modified Integral Images,
IEICE(E100-D), No. 1, January 2017, pp. 229-233.
WWW Link.
1701
BibRef
Huang, S.,
Wang, W.,
He, S.F.[Sheng-Feng],
Lau, R.W.H.[Rynson W.H.],
Stereo Object Proposals,
IP(26), No. 2, February 2017, pp. 671-683.
IEEE DOI
1702
object detection
BibRef
Huang, S.,
Wang, W.,
He, S.F.[Sheng-Feng],
Lau, R.W.H.[Rynson W.H.],
Egocentric Temporal Action Proposals,
IP(27), No. 2, February 2018, pp. 764-777.
IEEE DOI
1712
Atom optics, Cameras, Optical computing, Optical imaging, Proposals,
Videos, Temporal action proposals, actionness estimation,
temporal actionness network
BibRef
Ramesh, B.,
Xiang, C.,
Lee, T.H.,
Multiple object cues for high performance vector quantization,
PR(67), No. 1, 2017, pp. 380-395.
Elsevier DOI
1704
Log-polar transform
BibRef
Li, J.A.[Jian-An],
Wei, Y.C.[Yun-Chao],
Liang, X.D.[Xiao-Dan],
Dong, J.[Jian],
Xu, T.F.[Ting-Fa],
Feng, J.S.[Jia-Shi],
Yan, S.C.[Shui-Cheng],
Attentive Contexts for Object Detection,
MultMed(19), No. 5, May 2017, pp. 944-954.
IEEE DOI
1704
Context for object detection.
BibRef
Li, J.A.[Jian-An],
Liang, X.D.[Xiao-Dan],
Shen, S.M.[Sheng-Mei],
Xu, T.F.[Ting-Fa],
Feng, J.S.[Jia-Shi],
Yan, S.C.[Shui-Cheng],
Scale-Aware Fast R-CNN for Pedestrian Detection,
MultMed(20), No. 4, April 2018, pp. 985-996.
IEEE DOI
1804
Detectors, Feature extraction, Logic gates, Proposals, Robustness,
Skeleton, Training, Pedestrian detection, deep learning, scale-aware
BibRef
Li, J.A.[Jian-An],
Liang, X.D.[Xiao-Dan],
Wei, Y.C.[Yun-Chao],
Xu, T.F.[Ting-Fa],
Feng, J.S.[Jia-Shi],
Yan, S.C.[Shui-Cheng],
Perceptual Generative Adversarial Networks for Small Object Detection,
CVPR17(1951-1959)
IEEE DOI
1711
Feature extraction, Gallium nitride, Generators, Image resolution,
Object detection, Training
BibRef
Wang, J.[Jing],
Shen, J.[Jie],
Li, P.[Ping],
Object proposal with kernelized partial ranking,
PR(69), No. 1, 2017, pp. 299-309.
Elsevier DOI
1706
Object proposal
BibRef
Li, W.[Wei],
Li, H.L.[Hong-Liang],
Luo, B.[Bing],
Shi, H.C.[Heng-Can],
Wu, Q.B.[Qing-Bo],
Ngan, K.N.[King Ngi],
Improving object proposals with top-down cues,
SP:IC(56), No. 1, 2017, pp. 20-27.
Elsevier DOI
1706
Object, proposals
BibRef
Tang, S.,
Li, Y.,
Deng, L.,
Zhang, Y.,
Object Localization Based on Proposal Fusion,
MultMed(19), No. 9, September 2017, pp. 2105-2116.
IEEE DOI
1708
Complexity theory, Feature extraction, Object detection, Proposals,
Search problems, Testing, Training, Dense proposal fusion,
object detection, object localization, region proposal
BibRef
Jie, Z.[Zequn],
Lu, W.F.[Wen Feng],
Sakhavi, S.[Siavash],
Wei, Y.C.[Yun-Chao],
Tay, E.H.F.[Eng Hock Francis],
Yan, S.C.[Shui-Cheng],
Object Proposal Generation With Fully Convolutional Networks,
CirSysVideo(28), No. 1, January 2018, pp. 62-75.
IEEE DOI
1801
Image edge detection, Object detection, Pipelines, Proposals,
Semantics, Support vector machines, Testing,
object proposals
BibRef
Li, Y.[Yu],
Tang, S.[Sheng],
Lin, M.[Min],
Zhang, Y.D.[Yong-Dong],
Li, J.T.[Jin-Tao],
Yan, S.C.[Shui-Cheng],
Implicit Negative Sub-Categorization and Sink Diversion for Object
Detection,
IP(27), No. 4, April 2018, pp. 1561-1574.
IEEE DOI
1802
feature extraction, image classification, image representation,
image segmentation, object detection, probability, VOC,
faster R-CNN
BibRef
Guo, G.,
Wang, H.,
Zhao, W.L.,
Yan, Y.,
Li, X.,
Object Discovery via Cohesion Measurement,
Cyber(48), No. 3, March 2018, pp. 862-875.
IEEE DOI
1802
Distortion, Eigenvalues and eigenfunctions, Image color analysis,
Image segmentation, Laplace equations, Proposals, Robustness,
spectral clustering
BibRef
Murtaza, F.,
Yousaf, M.H.,
Velastin, S.A.,
PMHI: Proposals From Motion History Images for Temporal Segmentation
of Long Uncut Videos,
SPLetters(25), No. 2, February 2018, pp. 179-183.
IEEE DOI
1802
computer vision, image motion analysis, image segmentation,
image sequences, learning (artificial intelligence),
uncut videos
BibRef
Kuang, H.,
Yang, K.F.,
Chen, L.,
Li, Y.J.,
Chan, L.L.H.,
Yan, H.,
Bayes Saliency-Based Object Proposal Generator for Nighttime Traffic
Images,
ITS(19), No. 3, March 2018, pp. 814-825.
IEEE DOI
1804
Feature extraction, Generators, Image edge detection, Proposals,
Support vector machines, Vehicle detection, Bayes rule,
saliency detection
BibRef
Zhu, H.,
Vial, R.,
Lu, S.,
Peng, X.,
Fu, H.,
Tian, Y.,
Cao, X.,
YoTube:
Searching Action Proposal Via Recurrent and Static Regression Networks,
IP(27), No. 6, June 2018, pp. 2609-2622.
IEEE DOI
1804
dynamic programming, image classification, image motion analysis,
learning (artificial intelligence), object detection,
object detection
BibRef
Yang, J.[Jiong],
Yuan, J.S.[Jun-Song],
Temporally enhanced image object proposals for online video object
and action detections,
JVCIR(53), 2018, pp. 245-256.
Elsevier DOI
1805
Video, Proposal, Online, Detection, Temporal
BibRef
Li, J.,
Liang, X.,
Li, J.,
Wei, Y.,
Xu, T.,
Feng, J.,
Yan, S.,
Multistage Object Detection With Group Recursive Learning,
MultMed(20), No. 7, July 2018, pp. 1645-1655.
IEEE DOI
1806
Computer architecture, Feature extraction, Image segmentation,
Object detection, Proposals, Semantics, Image segmentation,
BibRef
Wang, J.[Juan],
Tao, X.M.[Xiao-Ming],
Xu, M.[Mai],
Duan, Y.P.[Yi-Ping],
Lu, J.H.[Jian-Hua],
Hierarchical objectness network for region proposal generation and
object detection,
PR(83), 2018, pp. 260-272.
Elsevier DOI
1808
Object detection, Object localization,
Region proposal generation, Convolutional neural network
BibRef
Huang, X.,
Zheng, Y.,
Huang, J.,
Zhang, Y.,
A Minimum Barrier Distance Based Saliency Box for Object Proposals
Generation,
SPLetters(25), No. 8, August 2018, pp. 1126-1130.
IEEE DOI
1808
computer vision, image segmentation, object detection,
minimum barrier distance based saliency box, MBDSal Box,
saliency box
BibRef
Tang, Y.X.[Yu-Xing],
Wang, J.[Josiah],
Wang, X.,
Gao, B.Y.[Bo-Yang],
Dellandréa, E.[Emmanuel],
Gaizauskas, R.[Robert],
Chen, L.M.[Li-Ming],
Visual and Semantic Knowledge Transfer for Large Scale
Semi-Supervised Object Detection,
PAMI(40), No. 12, December 2018, pp. 3045-3058.
IEEE DOI
1811
BibRef
Earlier: A1, A2, A4, A5, A6, A7, Only:
Large Scale Semi-Supervised Object Detection Using Visual and
Semantic Knowledge Transfer,
CVPR16(2119-2128)
IEEE DOI
1612
Semisupervised learning, Semantics,
Convolutional neural networks, Learning systems,
weakly supervised object detection
BibRef
Zhuge, Y.Z.[Yun-Zhi],
Yang, G.[Gang],
Zhang, P.P.[Ping-Ping],
Lu, H.C.[Hu-Chuan],
Boundary-Guided Feature Aggregation Network for Salient Object
Detection,
SPLetters(25), No. 12, December 2018, pp. 1800-1804.
IEEE DOI
1812
feature extraction, image enhancement, image resolution,
neural nets, object detection, multilevel feature maps,
salient object detection
BibRef
Zhang, X.N.[Xiao-Ning],
Wang, T.T.[Tian-Tian],
Qi, J.Q.[Jin-Qing],
Lu, H.C.[Hu-Chuan],
Wang, G.[Gang],
Progressive Attention Guided Recurrent Network for Salient Object
Detection,
CVPR18(714-722)
IEEE DOI
1812
Feature extraction, Semantics, Object detection,
Saliency detection, Task analysis, Estimation, Convolutional neural networks
BibRef
Jian, M.[Muwei],
Zhao, R.X.[Run-Xia],
Sun, X.[Xin],
Luo, H.J.[Han-Jiang],
Zhang, W.Y.[Wen-Yin],
Zhang, H.X.[Hua-Xiang],
Dong, J.Y.[Jun-Yu],
Yin, Y.L.[Yi-Long],
Lam, K.M.[Kin-Man],
Saliency detection based on background seeds by object proposals and
extended random walk,
JVCIR(57), 2018, pp. 202-211.
Elsevier DOI
1812
Saliency detection, Object proposals, Background seeds, Extended random walk
BibRef
Zhang, X.,
Xiong, H.,
Lin, W.,
Tian, Q.,
Weak to Strong Detector Learning for Simultaneous Classification and
Localization,
CirSysVideo(29), No. 2, February 2019, pp. 418-432.
IEEE DOI
1902
Detectors, Training, Image representation, Task analysis,
Support vector machines, Optimization, Automobiles,
object localization
BibRef
Zhang, X.,
Feng, J.,
Xiong, H.,
Tian, Q.,
Zigzag Learning for Weakly Supervised Object Detection,
CVPR18(4262-4270)
IEEE DOI
1812
Training, Object detection, Reliability, Energy measurement,
Detectors, Image edge detection, Proposals
BibRef
Ke, W.[Wei],
Chen, J.[Jie],
Ye, Q.X.[Qi-Xiang],
Deep contour and symmetry scored object proposal,
PRL(119), 2019, pp. 172-179.
Elsevier DOI
1902
Object proposal, Super-pixel grouping, FCN, Proposal scoring
BibRef
Xiang, C.C.[Chen-Chao],
Yu, Z.[Zhou],
Zhu, S.[Suguo],
Yu, J.[Jun],
Yang, X.K.[Xiao-Kang],
End-to-end visual grounding via region proposal networks and bilinear
pooling,
IET-CV(13), No. 2, March 2019, pp. 131-138.
DOI Link
1902
BibRef
Zhang, D.W.[Ding-Wen],
Han, J.W.[Jun-Wei],
Zhao, L.[Long],
Meng, D.Y.[De-Yu],
Leveraging Prior-Knowledge for Weakly Supervised Object Detection Under
a Collaborative Self-Paced Curriculum Learning Framework,
IJCV(127), No. 4, April 2019, pp. 363-380.
Springer DOI
1903
BibRef
Dong, X.Y.[Xuan-Yi],
Zheng, L.[Liang],
Ma, F.[Fan],
Yang, Y.[Yi],
Meng, D.Y.[De-Yu],
Few-Example Object Detection with Model Communication,
PAMI(41), No. 7, July 2019, pp. 1641-1654.
IEEE DOI
1906
Training, Object detection, Detectors, Videos,
Semisupervised learning, Task analysis, Sun, Few-example learning,
convolutional neural network
BibRef
Li, H.Y.[Hong-Yang],
Liu, Y.[Yu],
Ouyang, W.L.[Wan-Li],
Wang, X.G.[Xiao-Gang],
Zoom Out-and-In Network with Map Attention Decision for Region Proposal
and Object Detection,
IJCV(127), No. 3, March 2019, pp. 225-238.
Springer DOI
1903
Anchors of different sizes require different features.
BibRef
Dai, B.[Bo],
Wang, Y.[Yanbo],
Yao, Y.[Yiyang],
Ye, W.[Weijing],
Chen, T.[Ting],
Efficient object analysis by leveraging deeply-trained object
proposals prediction model,
JVCIR(61), 2019, pp. 218-224.
Elsevier DOI
1906
Video surveillance, Deep model, Object, Moving target detection, Model learning
BibRef
Park, S.W.[Sung Woo],
Kwon, J.[Junseok],
Orthogonal object proposal and its application,
IET-CV(13), No. 4, June 2019, pp. 420-427.
DOI Link
1906
BibRef
Chen, M.,
Zhang, J.,
He, S.,
Yang, Q.,
Li, Q.,
Yang, M.,
Interactive Hierarchical Object Proposals,
CirSysVideo(29), No. 9, September 2019, pp. 2552-2566.
IEEE DOI
1909
Proposals, Object detection, Computer vision, Image segmentation,
Motion segmentation, Shape, Object proposal,
transfer learning
BibRef
Xiong, B.[Bo],
Jain, S.D.[Suyog Dutt],
Grauman, K.[Kristen],
Pixel Objectness: Learning to Segment Generic Objects Automatically
in Images and Videos,
PAMI(41), No. 11, November 2019, pp. 2677-2692.
IEEE DOI
1910
Image segmentation, Videos, Motion segmentation, Training, Proposals,
Object segmentation, Task analysis, Image segmentation,
foreground segmentation
BibRef
Shen, Y.,
Ji, R.,
Yang, K.,
Deng, C.,
Wang, C.,
Category-Aware Spatial Constraint for Weakly Supervised Detection,
IP(29), No. 1, 2020, pp. 843-858.
IEEE DOI
1910
Proposals, Shape, Object detection, Image color analysis, Training,
Feature extraction, Detectors, Weakly supervised learning,
multi-center regularization
BibRef
Huang, X.,
Zheng, Y.,
Huang, J.,
Zhang, Y.,
50 FPS Object-Level Saliency Detection via Maximally Stable Region,
IP(29), No. , 2020, pp. 1384-1396.
IEEE DOI
1911
Saliency detection, Proposals, Object detection, Graphical models,
Visual systems, Deep learning, Visualization, Saliency detection,
seed selection
BibRef
Chen, H.,
Wang, Y.,
Wang, G.,
Bai, X.,
Qiao, Y.,
Progressive Object Transfer Detection,
IP(29), No. , 2020, pp. 986-1000.
IEEE DOI
1911
Detectors, Object detection, Proposals, Task analysis,
Benchmark testing, Deep learning, Labeling, Object detection,
low-shot learning
BibRef
Guo, L.,
Fan, G.,
Sheng, W.,
Creating 3D Bounding Box Hypotheses From Deep Network Score-Maps,
ICIP19(904-908)
IEEE DOI
1910
object detection in RGB-D, bounding box generation,
semantic labeling, deep learning
BibRef
Nabati, R.,
Qi, H.,
RRPN: Radar Region Proposal Network for Object Detection in
Autonomous Vehicles,
ICIP19(3093-3097)
IEEE DOI
1910
Region Proposal Network, Autonomous Driving, Object Detection
BibRef
Lee, S.[Seungkwan],
Kwak, S.[Suha],
Cho, M.[Minsu],
Universal Bounding Box Regression and Its Applications,
ACCV18(VI:373-387).
Springer DOI
1906
BibRef
Wilms, C.[Christian],
Frintrop, S.[Simone],
AttentionMask: Attentive, Efficient Object Proposal Generation Focusing
on Small Objects,
ACCV18(II:678-694).
Springer DOI
1906
BibRef
Tychsen-Smith, L.[Lachlan],
Petersson, L.[Lars],
Improving Object Localization with Fitness NMS and Bounded IoU Loss,
CVPR18(6877-6885)
IEEE DOI
1812
Evaluate bounding boxes.
Training, Detectors, Clustering algorithms, Testing,
Object recognition, Upper bound, Object detection
BibRef
Chen, K.[Kan],
Gao, J.[Jiyang],
Nevatia, R.[Ram],
Knowledge Aided Consistency for Weakly Supervised Phrase Grounding,
CVPR18(4042-4050)
IEEE DOI
1812
Visualization, Proposals, Grounding, Image reconstruction,
Feature extraction, Training, Task analysis
BibRef
Zhai, Y.,
Fu, J.,
Lu, Y.,
Li, H.,
Feature Selective Networks for Object Detection,
CVPR18(4139-4147)
IEEE DOI
1812
Feature extraction, Proposals, Object detection, Detectors,
Dimensionality reduction, Visualization, Pattern recognition
BibRef
Zhao, F.,
Li, J.,
Zhao, J.,
Feng, J.,
Weakly Supervised Phrase Localization with Multi-scale Anchored
Transformer Network,
CVPR18(5696-5705)
IEEE DOI
1812
Proposals, Training, Dogs, Computational modeling,
Image reconstruction, Image edge detection, Visualization
BibRef
Yeh, R.A.,
Do, M.N.,
Schwing, A.G.,
Unsupervised Textual Grounding: Linking Words to Image Concepts,
CVPR18(6125-6134)
IEEE DOI
1812
Grounding, Task analysis, Visualization, Proposals, Training,
Object detection, Feature extraction
BibRef
Peng, C.,
Xiao, T.,
Li, Z.,
Jiang, Y.,
Zhang, X.,
Jia, K.,
Yu, G.,
Sun, J.,
MegDet: A Large Mini-Batch Object Detector,
CVPR18(6181-6189)
IEEE DOI
1812
Training, Detectors, Object detection, Proposals, Convergence,
Task analysis, Industries
BibRef
Yao, Y.,
Dong, Y.,
Huang, Z.,
Bai, H.,
Dense Receptive Field for Object Detection,
ICPR18(1815-1820)
IEEE DOI
1812
Feature extraction, Detectors, Object detection, Proposals,
Computational efficiency, Fuses, Neural networks
BibRef
Lyu, J.,
Yuan, Z.,
Chen, D.,
Zhao, Y.,
Zhang, H.,
Learning Fixation Point Strategy for Object Detection and
Classification,
ICPR18(2081-2086)
IEEE DOI
1812
Task analysis, Stochastic processes, Object detection, Proposals,
Detectors, Automobiles
BibRef
Wang, H.[Hao],
Wang, Q.L.[Qi-Long],
Gao, M.Q.[Ming-Qi],
Li, P.H.[Pei-Hua],
Zuo, W.M.[Wang-Meng],
Multi-scale Location-Aware Kernel Representation for Object Detection,
CVPR18(1248-1257)
IEEE DOI
1812
Object detection, Kernel, Convolution, Proposals, Feature extraction,
Benchmark testing, Computer vision
BibRef
Wang, H.Y.[Han-Yuan],
Xu, J.[Jie],
Li, L.K.[Lin-Ke],
Tian, Y.[Ye],
Xu, D.[Du],
Xu, S.Z.[Shi-Zhong],
Multi-Scale Fusion with Context-Aware Network for Object Detection,
ICPR18(2486-2491)
IEEE DOI
1812
Proposals, Feature extraction, Object detection, Detectors,
Semantics, Computational efficiency, Convolution
BibRef
Razinkov, E.,
Saveleva, I.,
Matas, J.G.,
ALFA: Agglomerative Late Fusion Algorithm for Object Detection,
ICPR18(2594-2599)
IEEE DOI
1812
Detectors, Proposals, Object detection, Feature extraction,
Convolutional codes, Prediction algorithms, Heuristic algorithms
BibRef
Galteri, L.,
Bertini, M.,
Seidenari, L.,
del Bimbo, A.[Alberto],
Video Compression for Object Detection Algorithms,
ICPR18(3007-3012)
IEEE DOI
1812
Visualization, Streaming media, Encoding, Proposals, Bit rate,
Video coding, Detectors
BibRef
Rao, Y.,
Lin, D.,
Lu, J.,
Zhou, J.,
Learning Globally Optimized Object Detector via Policy Gradient,
CVPR18(6190-6198)
IEEE DOI
1812
Detectors, Object detection, Training, Proposals, Optimization,
Feature extraction, Task analysis
BibRef
Zhao, X.,
Liang, S.,
Wei, Y.,
Pseudo Mask Augmented Object Detection,
CVPR18(4061-4070)
IEEE DOI
1812
Image segmentation, Object detection, Task analysis,
Object segmentation, Training, Optimization, Network architecture
BibRef
Pirinen, A.,
Sminchisescu, C.,
Deep Reinforcement Learning of Region Proposal Networks for Object
Detection,
CVPR18(6945-6954)
IEEE DOI
1812
Proposals, Detectors, Search problems, Object detection,
Task analysis, History
BibRef
Cheng, J.,
Tsai, Y.,
Hung, W.,
Wang, S.,
Yang, M.,
Fast and Accurate Online Video Object Segmentation via Tracking Parts,
CVPR18(7415-7424)
IEEE DOI
1812
Object segmentation, Target tracking, Task analysis, Proposals,
Image segmentation, Strain
BibRef
Chen, Z.[Zhe],
Huang, S.[Shaoli],
Tao, D.C.[Da-Cheng],
Context Refinement for Object Detection,
ECCV18(VIII: 74-89).
Springer DOI
1810
BibRef
Tang, P.[Peng],
Wang, X.G.[Xing-Gang],
Wang, A.[Angtian],
Yan, Y.L.[Yong-Luan],
Liu, W.Y.[Wen-Yu],
Huang, J.Z.[Jun-Zhou],
Yuille, A.L.[Alan L.],
Weakly Supervised Region Proposal Network and Object Detection,
ECCV18(XI: 370-386).
Springer DOI
1810
BibRef
Uehara, K.[Kohei],
Tejero-De-Pablos, A.[Antonio],
Ushiku, Y.[Yoshitaka],
Harada, T.[Tatsuya],
Visual Question Generation for Class Acquisition of Unknown Objects,
ECCV18(XII: 492-507).
Springer DOI
1810
Code, dataset:
WWW Link.
BibRef
Wu, X.,
Ma, X.,
Zhang, J.,
Wang, A.,
Jin, Z.,
Salient Object Detection Via Deformed Smoothness Constraint,
ICIP18(2815-2819)
IEEE DOI
1809
Object detection, Image edge detection, Standards,
Noise measurement, Proposals, Deformable models, Visualization,
map refinement
BibRef
Dai, S.L.[Shuang-Lu],
Su, P.Y.[Peng-Yu],
Man, H.[Hong],
Object Discovery and Localization Via Structural Contrast,
ICIP18(2760-2764)
IEEE DOI
1809
Proposals, Adaptation models, Image edge detection, Visualization,
Data models, Measurement, Semantics, Object discovery,
Structural contrast
BibRef
Kaya, E.C.,
Alatan, A.A.,
Improving Proposal-Based Object Detection Using Convolutional Context
Features,
ICIP18(1308-1312)
IEEE DOI
1809
Feature extraction, Proposals, Context modeling, Training,
Object detection, Computer vision, Conferences, CNN,
Deep Learning
BibRef
Kolesnikov, A.[Alexander],
Lampert, C.[Christoph],
Improving Weakly-Supervised Object Localization By Micro-Annotation,
BMVC16(xx-yy).
HTML Version.
1805
BibRef
Rhodes, A.D.,
Witte, J.,
Jednyak, B.,
Mitchell, M.,
Bayesian optimization for refining object proposals,
IPTA17(1-7)
IEEE DOI
1804
Bayes methods, feature extraction, feedforward neural nets,
image classification, image representation, object detection,
Object localization
BibRef
Teh, E.W.[Eu Wern],
Rochan, M.[Mrigank],
Wang, Y.[Yang],
Attention Networks for Weakly Supervised Object Localization,
BMVC16(xx-yy).
HTML Version.
1805
BibRef
Teh, E.W.,
Guo, Z.,
Wang, Y.,
Object localization in weakly labeled data using regularized
attention networks,
VCIP17(1-4)
IEEE DOI
1804
object detection, attention scores, object detector,
object proposals, regularization term,
weakly supervised object localization
BibRef
Zhou, L.,
Fang, J.,
Ju, Y.,
Xue, J.,
Multi-Saliency Detection via Instance Specific Element Homology,
DICTA17(1-8)
IEEE DOI
1804
image colour analysis, image matching, image segmentation,
object detection, optimisation, probability, ISEH,
Proposals
BibRef
Liu, J.[Juan],
Wu, Z.Y.[Zheng-Yang],
Li, F.X.[Fu-Xin],
Ranking video segments with LSTM and determinantal point processes,
ICIP17(2369-2373)
IEEE DOI
1803
Feature extraction, Image segmentation, Logic gates,
Motion segmentation, Prediction algorithms, Proposals, Training, DPP,
Video Segmentation
BibRef
Zhou, X.Q.,
Zou, Y.X.,
Wang, Y.,
Accurate small object detection via density map aided saliency
estimation,
ICIP17(425-429)
IEEE DOI
1803
Estimation, Image reconstruction, Image segmentation,
Object detection, Task analysis, Training,
superpixel
BibRef
Mukherjee, P.,
Lall, B.,
Tandon, S.,
Salprop: Salient object proposals via aggregated edge cues,
ICIP17(2423-2429)
IEEE DOI
1803
Bayes methods, Image color analysis, Image edge detection,
Image segmentation, Microsoft Windows, Proposals, Training, CRF,
object proposals
BibRef
Malik, J.,
Aytekin, C.,
Gabbouj, M.,
Category independent object proposals using quantum superposition,
ICIP17(4172-4176)
IEEE DOI
1803
Computational efficiency, Eigenvalues and eigenfunctions,
Image segmentation, Microsoft Windows, Object detection, Proposals,
quantum superposition
BibRef
Wang, T.,
Context Propagation from Proposals for Semantic Video Object
Segmentation,
ICIP18(256-260)
IEEE DOI
1809
BibRef
And:
Submodular video object proposal selection for semantic object
segmentation,
ICIP17(4522-4526)
IEEE DOI
1803
Semantics, Proposals, Context modeling, Labeling,
Object segmentation, Convergence, Object detection,
semantic video object segmentation.
Cows, Image color analysis, Motion segmentation, Noise measurement,
Submodular function.
BibRef
Ye, L.,
Liu, Z.,
Wang, Y.,
Depth-aware object instance segmentation,
ICIP17(325-329)
IEEE DOI
1803
Detectors, Estimation, Image resolution, Image segmentation,
Object detection, Proposals, Semantics, depth, instance segmentation,
occlusion reasoning
BibRef
Qiao, S.,
Shen, W.,
Qiu, W.,
Liu, C.,
Yuille, A.L.[Alan L.],
ScaleNet:
Guiding Object Proposal Generation in Supermarkets and Beyond,
ICCV17(1809-1818)
IEEE DOI
1802
object detection, MS COCO dataset, ScaleNet,
natural images, object proposal generation,
Virtual environments
BibRef
Zhu, Y.,
Zhou, Y.,
Ye, Q.,
Qiu, Q.,
Jiao, J.,
Soft Proposal Networks for Weakly Supervised Object Localization,
ICCV17(1859-1868)
IEEE DOI
1802
feedforward neural nets, image classification,
image representation, learning (artificial intelligence),
Visualization
BibRef
Ma, J.,
Ming, A.,
Huang, Z.,
Wang, X.,
Zhou, Y.,
Object-Level Proposals,
ICCV17(4931-4939)
IEEE DOI
1802
edge detection, object detection, VOC, object detection,
object-level proposal model, object-level proposals,
Visualization
BibRef
Xu, H.[Hongyu],
Lv, X.[Xutao],
Wang, X.[Xiaoyu],
Ren, Z.[Zhou],
Bodla, N.[Navaneeth],
Chellappa, R.[Rama],
Deep Regionlets for Object Detection,
ECCV18(XI: 827-844).
Springer DOI
1810
BibRef
Singh, B.,
Davis, L.S.,
An Analysis of Scale Invariance in Object Detection - SNIP,
CVPR18(3578-3587)
IEEE DOI
1812
Image resolution, Training, Detectors, Object detection,
Feature extraction, Convolution, Semantics
BibRef
Bodla, N.,
Singh, B.,
Chellappa, R.,
Davis, L.S.,
Soft-NMS: Improving Object Detection with One Line of Code,
ICCV17(5562-5570)
IEEE DOI
1802
computational complexity,
learning (artificial intelligence), neural nets,
Proposals
BibRef
Chen, X.,
Gupta, A.,
Spatial Memory for Context Reasoning in Object Detection,
ICCV17(4106-4116)
IEEE DOI
1802
image sequences, inference mechanisms,
learning (artificial intelligence), neural nets,
Proposals
BibRef
Portaz, M.,
Kohl, M.,
Quénot, G.,
Chevallet, J.P.,
Fully Convolutional Network and Region Proposal for Instance
Identification with Egocentric Vision,
Egocentric17(2383-2391)
IEEE DOI
1802
Cameras, Image representation, Image retrieval, Painting, Proposals,
Search problems
BibRef
Hu, H.,
Lan, S.,
Jiang, Y.,
Cao, Z.,
Sha, F.,
FastMask: Segment Multi-scale Object Candidates in One Shot,
CVPR17(2280-2288)
IEEE DOI
1711
Feature extraction, Head, Image segmentation, Neck, Proposals, Semantics
BibRef
Deng, Z.,
Latecki, L.J.,
Amodal Detection of 3D Objects:
Inferring 3D Bounding Boxes from 2D Ones in RGB-Depth Images,
CVPR17(398-406)
IEEE DOI
1711
Feature extraction, Object detection, Proposals, Solid modeling,
Two, dimensional, displays
BibRef
Abbeloos, W.,
Caccamo, S.,
Ataer-Cansizoglu, E.,
Taguchi, Y.,
Feng, C.,
Lee, T.Y.,
Detecting and Grouping Identical Objects for Region Proposal and
Classification,
DeepLearnRV17(501-502)
IEEE DOI
1709
Clustering algorithms, Computer vision,
Object detection, Object recognition, Pipelines, Proposals
BibRef
Li, S.,
Zhang, H.,
Zhang, J.,
Ren, Y.,
Kuo, C.C.J.,
Box Refinement: Object Proposal Enhancement and Pruning,
WACV17(979-988)
IEEE DOI
1609
Detectors, Feature extraction, Image edge detection,
Neural networks, Proposals, Search, problems
BibRef
Lauri, M.[Mikko],
Frintrop, S.[Simone],
Object Proposal Generation Applying the Distance Dependent Chinese
Restaurant Process,
SCIA17(I: 260-272).
Springer DOI
1706
BibRef
Waris, M.A.,
Iosifidis, A.,
Gabbouj, M.,
Object proposals using CNN-based edge filtering,
ICPR16(627-632)
IEEE DOI
1705
Feature extraction, Image edge detection, Merging,
Object detection, Proposals, Semantics, Deep Learning,
Object Detection, Object Proposals, Region, Of, Interest
BibRef
Zhang, H.Y.[Hao-Yang],
He, X.M.[Xu-Ming],
Porikli, F.M.[Fatih M.],
Learning Spatial Transforms for Refining Object Segment Proposals,
WACV17(37-46)
IEEE DOI
1609
BibRef
Earlier:
Learning to Generate Object Segment Proposals with Multi-modal Cues,
ACCV16(I: 121-136).
Springer DOI
1704
Feature extraction, Image segmentation, Pipelines, Proposals,
Semantics, Transforms, Two, dimensional, displays
BibRef
Shi, W.,
Zhu, H.,
Yang, L.,
Luo, Y.,
Shape based co-segmentation repairing by segment evaluation and
object proposals,
VCIP16(1-4)
IEEE DOI
1701
Computational modeling
BibRef
Zhang, R.,
Wang, W.,
An advanced local offset matching strategy for object proposal
matching,
VCIP16(1-4)
IEEE DOI
1701
Bayes methods
BibRef
Knaub, A.[Anton],
Narayan, V.[Vikram],
Adameck, M.[Markus],
Performance Evaluation of Bottom-Up Saliency Models for Object
Proposal Generation,
CRV16(266-272)
IEEE DOI
1612
Object proposal generation
BibRef
Ke, W.,
Zhang, T.,
Chen, J.,
Wan, F.,
Ye, Q.,
Han, Z.,
Texture Complexity Based Redundant Regions Ranking for Object
Proposal,
Robust16(1083-1091)
IEEE DOI
1612
BibRef
Zhang, Y.,
Jiang, Z.,
Chen, X.,
Davis, L.S.,
Generating Discriminative Object Proposals via Submodular Ranking,
Robust16(1168-1176)
IEEE DOI
1612
BibRef
Singh, K.K.[Krishna Kumar],
Lee, Y.J.[Yong Jae],
Hide-and-Seek: Forcing a Network to be Meticulous for
Weakly-Supervised Object and Action Localization,
ICCV17(3544-3553)
IEEE DOI
1802
image classification, image representation,
learning (artificial intelligence), object detection,
Visualization
BibRef
Singh, K.K.[Krishna Kumar],
Xiao, F.Y.[Fan-Yi],
Lee, Y.J.[Yong Jae],
Track and Transfer: Watching Videos to Simulate Strong Human
Supervision for Weakly-Supervised Object Detection,
CVPR16(3548-3556)
IEEE DOI
1612
BibRef
Li, D.[Dong],
Huang, J.B.[Jia-Bin],
Li, Y.[Yali],
Wang, S.J.[Sheng-Jin],
Yang, M.H.[Ming-Hsuan],
Weakly Supervised Object Localization with Progressive Domain
Adaptation,
CVPR16(3512-3520)
IEEE DOI
1612
Image level annotation, not location.
BibRef
Sun, C.[Chen],
Paluri, M.[Manohar],
Collobert, R.[Ronan],
Nevatia, R.[Ram],
Bourdev, L.[Lubomir],
ProNet:
Learning to Propose Object-Specific Boxes for Cascaded Neural Networks,
CVPR16(3485-3493)
IEEE DOI
1612
BibRef
Pham, T.T.,
Rezatofighi, S.H.,
Reid, I.D.,
Chin, T.J.,
Efficient Point Process Inference for Large-Scale Object Detection,
CVPR16(2837-2845)
IEEE DOI
1612
BibRef
Lu, Y.X.[Yong-Xi],
Javidi, T.[Tara],
Lazebnik, S.[Svetlana],
Adaptive Object Detection Using Adjacency and Zoom Prediction,
CVPR16(2351-2359)
IEEE DOI
1612
BibRef
Kong, T.,
Yao, A.,
Chen, Y.,
Sun, F.,
HyperNet: Towards Accurate Region Proposal Generation and Joint
Object Detection,
CVPR16(845-853)
IEEE DOI
1612
BibRef
Chavali, N.,
Agrawal, H.,
Mahendru, A.,
Batra, D.,
Object-Proposal Evaluation Protocol is 'Gameable',
CVPR16(835-844)
IEEE DOI
1612
BibRef
Zeng, X.Y.[Xing-Yu],
Ouyang, W.L.[Wan-Li],
Yang, B.[Bin],
Yan, J.J.[Jun-Jie],
Wang, X.G.[Xiao-Gang],
Gated Bi-Directional CNN for Object Detection,
ECCV16(VII: 354-369).
Springer DOI
1611
BibRef
Tiwari, L.[Lokender],
Anand, S.[Saket],
DGSAC: Density Guided Sampling and Consensus,
WACV18(974-982)
IEEE DOI
1806
computational geometry, computer vision, image reconstruction,
image segmentation, matrix algebra, DGSAC,
Robustness
BibRef
Tiwari, L.[Lokender],
Anand, S.[Saket],
Mittal, S.[Sushil],
Robust Multi-Model Fitting Using Density and Preference Analysis,
ACCV16(IV: 308-323).
Springer DOI
1704
BibRef
Tiwari, L.[Lokender],
Anand, S.[Saket],
Fast hypothesis filtering for multi-structure geometric model fitting,
ICIP16(3728-3732)
IEEE DOI
1610
Clustering algorithms
BibRef
Bappy, J.H.,
Roy-Chowdhury, A.K.,
Inter-dependent CNNs for joint scene and object recognition,
ICPR16(3386-3391)
IEEE DOI
1705
BibRef
And:
CNN based region proposals for efficient object detection,
ICIP16(3658-3662)
IEEE DOI
1610
Detectors, Feature extraction, Neural networks, Object detection,
Object recognition, Proposals.
BibRef
Paul, S.,
Bappy, J.H.,
Roy-Chowdhury, A.K.,
Efficient selection of informative and diverse training samples with
applications in scene classification,
ICIP16(494-498)
IEEE DOI
1610
Computational modeling
BibRef
Horiguchi, S.,
Aizawa, K.,
Ogawa, M.,
The log-normal distribution of the size of objects in daily meal
images and its application to the efficient reduction of object
proposals,
ICIP16(3668-3672)
IEEE DOI
1610
Gaussian distribution
BibRef
Zhang, H.,
He, X.,
Porikli, F.M.,
Kneip, L.,
Semantic context and depth-aware object proposal generation,
ICIP16(1-5)
IEEE DOI
1610
Context
BibRef
Peng, L.,
Qi, X.,
Temporal objectness: Model-free learning of object proposals in video,
ICIP16(3663-3667)
IEEE DOI
1610
Detectors
BibRef
Werner, T.[Thomas],
Martín-García, G.[Germán],
Frintrop, S.[Simone],
Saliency-Guided Object Candidates Based on Gestalt Principles,
CVS15(34-44).
Springer DOI
1507
BibRef
Klein, D.A.[Dominik Alexander],
Frintrop, S.[Simone],
Salient Pattern Detection Using W2 on Multivariate Normal Distributions,
DAGM12(246-255).
Springer DOI
1209
BibRef
Klein, D.A.[Dominik Alexander],
Schulz, D.[Dirk],
Frintrop, S.[Simone],
Boosting with a Joint Feature Pool from Different Sensors,
CVS09(63-72).
Springer DOI
0910
BibRef
Frintrop, S.,
The high repeatability of salient regions,
ViA08(xx-yy).
0810
BibRef
Lee, T.,
Fidler, S.,
Dickinson, S.,
Learning to Combine Mid-Level Cues for Object Proposal Generation,
ICCV15(1680-1688)
IEEE DOI
1602
Adaptation models
BibRef
Zhu, H.Y.[Hong-Yuan],
Lu, S.J.[Shi-Jian],
Cai, J.F.[Jian-Fei],
Lee, G.Q.[Guang-Qing],
Diagnosing state-of-the-art object proposal methods,
BMVC15(xx-yy).
DOI Link
1601
See also How good are detection proposals, really?.
BibRef
Chen, X.Z.[Xiao-Zhi],
Ma, H.M.[Hui-Min],
Wang, X.[Xiang],
Zhao, Z.C.[Zhi-Chen],
Improving object proposals with multi-thresholding straddling
expansion,
CVPR15(2587-2595)
IEEE DOI
1510
BibRef
Liu, S.[Shu],
Lu, C.[Cewu],
Jia, J.[Jiaya],
Box Aggregation for Proposal Decimation:
Last Mile of Object Detection,
ICCV15(2569-2577)
IEEE DOI
1602
Computational modeling
BibRef
Pont-Tuset, J.[Jordi],
van Gool, L.J.[Luc J.],
Boosting Object Proposals: From Pascal to COCO,
ICCV15(1546-1554)
IEEE DOI
1602
Survey of techniques and impact of changing standard benchmark datasets.
See also COCO: Common Objects in Context.
See also PASCAL Visual Object Classes Challenge 2012, The.
BibRef
Manen, S.[Santiago],
Guillaumin, M.[Matthieu],
Van Gool, L.J.[Luc J.],
Prime Object Proposals with Randomized Prim's Algorithm,
ICCV13(2536-2543)
IEEE DOI
1403
Object Detection; Object Proposal
BibRef
Ristin, M.[Marko],
Gall, J.[Juergen],
Van Gool, L.J.[Luc J.],
Local Context Priors for Object Proposal Generation,
ACCV12(I:57-70).
Springer DOI
1304
Selective search to get hypotheses
BibRef
He, S.F.[Sheng-Feng],
Lau, R.W.H.[Rynson W. H.],
Oriented Object Proposals,
ICCV15(280-288)
IEEE DOI
1602
Detectors
BibRef
Kwak, S.[Suha],
Cho, M.[Minsu],
Laptev, I.,
Ponce, J.[Jean],
Schmid, C.[Cordelia],
Unsupervised Object Discovery and Tracking in Video Collections,
ICCV15(3173-3181)
IEEE DOI
1602
BibRef
And: A2, A1, A5, A4, Only:
Unsupervised object discovery and localization in the wild:
Part-based matching with bottom-up region proposals,
CVPR15(1201-1210)
IEEE DOI
1510
Coherence.
dominant objects from a noisy image collection with multiple object classes.
BibRef
Zitnick, C.L.[C. Lawrence],
Dollár, P.[Piotr],
Edge Boxes: Locating Object Proposals from Edges,
ECCV14(V: 391-405).
Springer DOI
1408
BibRef
Krähenbühl, P.[Philipp],
Koltun, V.[Vladlen],
Geodesic Object Proposals,
ECCV14(V: 725-739).
Springer DOI
1408
BibRef
Rantalankila, P.[Pekka],
Kannala, J.H.[Ju-Ho],
Rahtu, E.[Esa],
Generating Object Segmentation Proposals Using Global and Local
Search,
CVPR14(2417-2424)
IEEE DOI
1409
Object detection
BibRef
Bonev, B.[Boyan],
Yuille, A.L.[Alan L.],
A Fast and Simple Algorithm for Producing Candidate Regions,
ECCV14(III: 535-549).
Springer DOI
1408
e.g. initial bounding box?
BibRef
Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Maximally Stable Extremal Regions, MSER Descriptions .