7.1.10.3 Object Proposals, Initial Points, Proto-Objects, Candidates

Chapter Contents (Back)
Object Detection. Object Proposals. Proposals.
See also Instance of Particular Object, Specified Object.

Yanulevskaya, V.[Victoria], 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

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

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

Kong, Y.Q.[Yu-Qiu], Feng, M.Y.[Meng-Yang], Li, X.[Xin], Lu, H.C.[Hu-Chuan], Liu, X.P.[Xiu-Ping], Yin, B.C.[Bao-Cai],
Spatial context-aware network for salient object detection,
PR(114), 2021, pp. 107867.
Elsevier DOI 2103
Salient object detection, Context-aware methods, Deep learning BibRef

Feng, M.Y.[Meng-Yang], Lu, H.C.[Hu-Chuan], Ding, E.[Errui],
Attentive Feedback Network for Boundary-Aware Salient Object Detection,
CVPR19(1623-1632).
IEEE DOI 2002
BibRef

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

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

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.B.[Yan-Bo], Yao, Y.Y.[Yi-Yang], Ye, W.J.[Wei-Jing], Chen, T.[Ting],
RETRACTED: Efficient object analysis by leveraging deeply-trained object proposals prediction model,
JVCIR(69), 2020, pp. 102837.
Elsevier DOI 2006
BibRef
And: Original: JVCIR(61), 2019, pp. 218-224. 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

Tang, P.[Peng], Wang, X.G.[Xing-Gang], Bai, S.[Song], Shen, W.[Wei], Bai, X.[Xiang], Liu, W.Y.[Wen-Yu], Yuille, A.L.[Alan L.],
PCL: Proposal Cluster Learning for Weakly Supervised Object Detection,
PAMI(42), No. 1, January 2020, pp. 176-191.
IEEE DOI 1912
Proposals, Training, Streaming media, Detectors, Object detection, Convolutional neural networks, Object detection, proposal cluster BibRef

Tang, P.[Peng], Ramaiah, C.[Chetan], Wang, Y.[Yan], Xu, R.[Ran], Xiong, C.[Caiming],
Proposal Learning for Semi-Supervised Object Detection,
WACV21(2290-2300)
IEEE DOI 2106
Training, Detectors, Object detection, Semisupervised learning, Feature extraction 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

Wang, J.W.[Jin-Wang], Ding, J.[Jian], Guo, H.[Haowen], Cheng, W.S.[Wen-Sheng], Pan, T.[Ting], Yang, W.[Wen],
Mask OBB: A Semantic Attention-Based Mask Oriented Bounding Box Representation for Multi-Category Object Detection in Aerial Images,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Tao, X.Y.[Xiao-Yu], Gong, Y.H.[Yi-Hong], Shi, W.W.[Wei-Wei], Cheng, D.[De],
Object detection with class aware region proposal network and focused attention objective,
PRL(130), 2020, pp. 353-361.
Elsevier DOI 2002
Convolutional neural networks, Object detection, Region proposal BibRef

Alqasir, H.[Hiba], Muselet, D.[Damien], Ducottet, C.[Christophe],
Region Proposal Oriented Approach for Domain Adaptive Object Detection,
ACIVS20(38-50).
Springer DOI 2003
BibRef

Tsai, C., Hsu, K., Lin, Y., Qian, X., Chuang, Y.,
Deep Co-Saliency Detection via Stacked Autoencoder-Enabled Fusion and Self-Trained CNNs,
MultMed(22), No. 4, April 2020, pp. 1016-1031.
IEEE DOI 2004
Proposals, Saliency detection, Image segmentation, Image reconstruction, Reliability, Task analysis, Fuses, CNNs BibRef

Kang, B.R.[Ba Rom], Lee, H.[Hyunku], Park, K.[Keunju], Ryu, H.[Hyunsurk], Kim, H.Y.[Ha Young],
BshapeNet: Object detection and instance segmentation with bounding shape masks,
PRL(131), 2020, pp. 449-455.
Elsevier DOI 2004
BibRef

Majelan, S.G.[Sina Ghofrani], Havaei, M.[Mohammad],
CAGNet: Content-Aware Guidance for Salient Object Detection,
PR(103), 2020, pp. 107303.
Elsevier DOI 2005
Saliency detection, Fully convolutional neural networks, Attention guidance BibRef

Zhang, D.W.[Ding-Wen], Han, J.W.[Jun-Wei], Guo, G.Y.[Guang-Yu], Zhao, L.[Long],
Learning Object Detectors With Semi-Annotated Weak Labels,
CirSysVideo(29), No. 12, December 2019, pp. 3622-3635.
IEEE DOI 1912
Training, Object detection, Detectors, Training data, Generators, Visualization, Semantics, Computer vision, image processing, learning (artificial intelligence) BibRef

Han, J.W.[Jun-Wei], Zhang, D.W.[Ding-Wen], Cheng, G.[Gong], Guo, L.[Lei], Ren, J.C.[Jin-Chang],
Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning,
GeoRS(53), No. 6, June 2015, pp. 3325-3337.
IEEE DOI 1503
feature extraction. Combine low level features with higher level grouping features. BibRef

Cheng, G.[Gong], Han, J.W.[Jun-Wei],
A survey on object detection in optical remote sensing images,
PandRS(117), No. 1, 2016, pp. 11-28.
Elsevier DOI 1605
Object detection BibRef

Cheng, G.[Gong], Zhou, P., Han, J.W.[Jun-Wei],
Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images,
GeoRS(54), No. 12, December 2016, pp. 7405-7415.
IEEE DOI 1612
BibRef
And:
RIFD-CNN: Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection,
CVPR16(2884-2893)
IEEE DOI 1612
image processing BibRef

Yao, X.[Xiwen], Feng, X.X.[Xiao-Xu], Han, J.W.[Jun-Wei], Cheng, G.[Gong], Guo, L.[Lei],
Automatic Weakly Supervised Object Detection From High Spatial Resolution Remote Sensing Images via Dynamic Curriculum Learning,
GeoRS(59), No. 1, January 2021, pp. 675-685.
IEEE DOI 2012
Training, Detectors, Remote sensing, Object detection, Proposals, Robustness, Spatial resolution, weakly supervised object detection (WSOD) BibRef

Li, K.[Ke], Wan, G.[Gang], Cheng, G.[Gong], Meng, L.Q.[Li-Qiu], Han, J.W.[Jun-Wei],
Object detection in optical remote sensing images: A survey and a new benchmark,
PandRS(159), 2020, pp. 296-307.
Elsevier DOI 1912
Object detection, Deep learning, Convolutional Neural Network (CNN), Benchmark dataset, Optical remote sensing images BibRef

Li, K.[Ke], Cheng, G.[Gong], Bu, S.H.[Shu-Hui], You, X.[Xiong],
Rotation-Insensitive and Context-Augmented Object Detection in Remote Sensing Images,
GeoRS(56), No. 4, April 2018, pp. 2337-2348.
IEEE DOI 1804
Context modeling, Feature extraction, Geospatial analysis, Object detection, Proposals, Remote sensing, Satellites, restricted Boltzmann machine (RBM) BibRef

Cheng, G.[Gong], Zhou, P., Han, J.W.[Jun-Wei],
Duplex Metric Learning for Image Set Classification,
IP(27), No. 1, January 2018, pp. 281-292.
IEEE DOI 1712
face recognition, image classification, image coding, image reconstruction, image representation, image sampling, metric learning BibRef

Cheng, G., Han, J., Zhou, P., Xu, D.,
Learning Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection,
IP(28), No. 1, January 2019, pp. 265-278.
IEEE DOI 1810
convolution, feature extraction, feedforward neural nets, image representation, learning (artificial intelligence), rotation invariance BibRef

Feng, X.X.[Xiao-Xu], Han, J.W.[Jun-Wei], Yao, X.W.[Xi-Wen], Cheng, G.[Gong],
Progressive Contextual Instance Refinement for Weakly Supervised Object Detection in Remote Sensing Images,
GeoRS(58), No. 11, November 2020, pp. 8002-8012.
IEEE DOI 2011
Proposals, Object detection, Remote sensing, Detectors, Feature extraction, Annotations, Training, weakly supervised object detection (WSOD) BibRef

Feng, X.X.[Xiao-Xu], Han, J.W.[Jun-Wei], Yao, X.W.[Xi-Wen], Cheng, G.[Gong],
TCANet: Triple Context-Aware Network for Weakly Supervised Object Detection in Remote Sensing Images,
GeoRS(59), No. 8, August 2021, pp. 6946-6955.
IEEE DOI 2108
Object detection, Proposals, Visualization, Remote sensing, Annotations, Semantics, Detectors, Context-aware network, weakly supervised object detection (WSOD) BibRef

Cheng, G.[Gong], Yang, J.Y.[Jun-Yu], Gao, D.C.[De-Cheng], Guo, L.[Lei], Han, J.W.[Jun-Wei],
High-Quality Proposals for Weakly Supervised Object Detection,
IP(29), 2020, pp. 5794-5804.
IEEE DOI 2005
Proposals, Training, Detectors, Object detection, Search problems, Task analysis, Convolutional neural networks, convolutional neural networks (CNNs) BibRef

Yao, X., Han, J., Cheng, G., Qian, X., Guo, L.,
Semantic Annotation of High-Resolution Satellite Images via Weakly Supervised Learning,
GeoRS(54), No. 6, June 2016, pp. 3660-3671.
IEEE DOI 1606
feature extraction BibRef

Cheng, G., Yang, C., Yao, X., Guo, L., Han, J.,
When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs,
GeoRS(56), No. 5, May 2018, pp. 2811-2821.
IEEE DOI 1805
Computer architecture, Feature extraction, Image color analysis, Learning systems, Machine learning, Measurement, Remote sensing, remote sensing image scene classification BibRef

Li, D.[Dong], Huang, J.B.[Jia-Bin], Li, Y.[Yali], Wang, S.J.[Sheng-Jin], Yang, M.H.[Ming-Hsuan],
Progressive Representation Adaptation for Weakly Supervised Object Localization,
PAMI(42), No. 6, June 2020, pp. 1424-1438.
IEEE DOI 2005
BibRef
Earlier:
Weakly Supervised Object Localization with Progressive Domain Adaptation,
CVPR16(3512-3520)
IEEE DOI 1612
Image level annotation, not location. Proposals, Detectors, Training, Feature extraction, Clutter, Noise measurement, Adaptation models, Weakly supervised learning, domain adaptation. BibRef

Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.,
FoveaBox: Beyound Anchor-Based Object Detection,
IP(29), 2020, pp. 7389-7398.
IEEE DOI 2007
Object detection, anchor free, foveabox BibRef

Tian, Z.Z.[Zhuang-Zhuang], Zhan, R.H.[Rong-Hui], Hu, J.[Jiemin], Wang, W.[Wei], He, Z.Q.[Zhi-Qiang], Zhuang, Z.W.[Zhao-Wen],
Generating Anchor Boxes Based on Attention Mechanism for Object Detection in Remote Sensing Images,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Ding, K.[Kun], He, G.J.[Guo-Jin], Gu, H.X.[Hu-Xiang], Zhong, Z.S.[Zi-Sha], Xiang, S.M.[Shi-Ming], Pan, C.H.[Chun-Hong],
Packdet: Packed Long-Head Object Detector,
ECCV20(XIII:172-188).
Springer DOI 2011
BibRef

Jiang, Z.K.[Zheng-Kai], Liu, Y.[Yu], Yang, C.[Ceyuan], Liu, J.[Jihao], Gao, P.[Peng], Zhang, Q.[Qian], Xiang, S.M.[Shi-Ming], Pan, C.H.[Chun-Hong],
Learning Where to Focus for Efficient Video Object Detection,
ECCV20(XVI: 18-34).
Springer DOI 2010
BibRef

Jin, Z.C.[Zhen-Chao], Liu, B.[Bin], Chu, Q.[Qi], Yu, N.H.[Neng-Hai],
SAFNet: A Semi-Anchor-Free Network With Enhanced Feature Pyramid for Object Detection,
IP(29), 2020, pp. 9445-9457.
IEEE DOI 2010
Feature extraction, Detectors, Object detection, Generators, Semantics, Task analysis, Training, Object detection, deep learning BibRef

Wang, H.[Hao], Wang, Q.L.[Qi-Long], Li, P.H.[Pei-Hua], Zuo, W.M.[Wang-Meng],
Multi-scale structural kernel representation for object detection,
PR(110), 2021, pp. 107593.
Elsevier DOI 2011
Object detection, High-order statistics, Polynomial kernel, Matrix power normalization 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

Wei, H.[Haoran], Zhang, Y.[Yue], Chang, Z.H.[Zhong-Han], Li, H.[Hao], Wang, H.Q.[Hong-Qi], Sun, X.[Xian],
Oriented objects as pairs of middle lines,
PandRS(169), 2020, pp. 268-279.
Elsevier DOI 2011
Object detection, Oriented objects, Middle lines, Anchor-free, NMS-free BibRef

Dou, Z., Gao, K., Zhang, X., Wang, H., Wang, J.,
Improving Performance and Adaptivity of Anchor-Based Detector Using Differentiable Anchoring With Efficient Target Generation,
IP(30), 2021, pp. 712-724.
IEEE DOI 2012
Detectors, Shape, Optimization, Training, Object detection, Feature extraction, Task analysis, Object detection, anchor. BibRef

Chen, Z.[Zhe], Zhang, J.[Jing], Tao, D.C.[Da-Cheng],
Recursive Context Routing for Object Detection,
IJCV(129), No. 1, January 2021, pp. 142-160.
Springer DOI 2101
Context in detection. BibRef

Chen, Z.[Zhe], Huang, S.[Shaoli], Tao, D.C.[Da-Cheng],
Context Refinement for Object Detection,
ECCV18(VIII: 74-89).
Springer DOI 1810
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Chen, X., Yu, J., Kong, S., Wu, Z., Wen, L.,
Joint Anchor-Feature Refinement for Real-Time Accurate Object Detection in Images and Videos,
CirSysVideo(31), No. 2, February 2021, pp. 594-607.
IEEE DOI 2102
Feature extraction, Head, Object detection, Videos, Detectors, Real-time systems, Task analysis, Object detection, deep learning BibRef

Xu, Y.C.[Yong-Chao], Fu, M.T.[Ming-Tao], Wang, Q.M.[Qi-Meng], Wang, Y.K.[Yu-Kang], Chen, K.[Kai], Xia, G.S.[Gui-Song], Bai, X.[Xiang],
Gliding Vertex on the Horizontal Bounding Box for Multi-Oriented Object Detection,
PAMI(43), No. 4, April 2021, pp. 1452-1459.
IEEE DOI 2103
Object detection, Feature extraction, Proposals, Detectors, Benchmark testing, Runtime, Object detection, R-CNN, pedestrian detection BibRef

Solovyev, R.[Roman], Wang, W.M.[Wei-Min], Gabruseva, T.[Tatiana],
Weighted boxes fusion: Ensembling boxes from different object detection models,
IVC(107), 2021, pp. 104117.
Elsevier DOI 2103
Object detection, Computer vision, Deep learning BibRef

Zou, W.B.[Wen-Bin], Zhang, Z.Y.[Zheng-Yu], Peng, Y.Q.[Ying-Qing], Xiang, C.Q.[Can-Qun], Tian, S.S.[Shi-Shun], Zhang, L.[Lu],
SC-RPN: A Strong Correlation Learning Framework for Region Proposal,
IP(30), 2021, pp. 4084-4098.
IEEE DOI 2104
Proposals, Correlation, Detectors, Task analysis, Training, Merging, Object detection, Region proposal, two-stage, strong correlation, SC-RPN BibRef

Wang, J.W.[Jin-Wang], Yang, W.[Wen], Li, H.C.[Heng-Chao], Zhang, H.J.[Hai-Jian], Xia, G.S.[Gui-Song],
Learning Center Probability Map for Detecting Objects in Aerial Images,
GeoRS(59), No. 5, May 2021, pp. 4307-4323.
IEEE DOI 2104
Task analysis, Image segmentation, Feature extraction, Semantics, Image color analysis, Object detection, Sensors, Aerial images, oriented bounding boxes (OBBs) BibRef

Xu, H.Y.[Hong-Yu], Lv, X.[Xutao], Wang, X.Y.[Xiao-Yu], Ren, Z.[Zhou], Bodla, N.[Navaneeth], Chellappa, R.[Rama],
Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection,
PAMI(43), No. 6, June 2021, pp. 1914-1927.
IEEE DOI 2106
BibRef
Earlier:
Deep Regionlets for Object Detection,
ECCV18(XI: 827-844).
Springer DOI 1810
Feature extraction, Detectors, Object detection, Proposals, Machine learning, Deformable models, Strain, Object detection, spatial transformation BibRef

Xu, Y.J.[You-Jiang], Zhu, L.C.[Lin-Chao], Yang, Y.[Yi], Wu, F.[Fei],
Training Robust Object Detectors From Noisy Category Labels and Imprecise Bounding Boxes,
IP(30), 2021, pp. 5782-5792.
IEEE DOI 2106
Noise measurement, Detectors, Annotations, Object detection, Training, Proposals, Feature extraction, Deep learning, object detection BibRef

Qu, Z.[Zhong], Zhang, R.[Run], Bao, K.H.[Kang-Hua],
A keypoint-based object detection method with wide dual-path backbone network and attention modules,
IET-IPR(15), No. 8, 2021, pp. 1800-1813.
DOI Link 2106
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Shi, S.S.[Shao-Shuai], Wang, Z.[Zhe], Shi, J.P.[Jian-Ping], Wang, X.G.[Xiao-Gang], Li, H.S.[Hong-Sheng],
From Points to Parts: 3D Object Detection From Point Cloud With Part-Aware and Part-Aggregation Network,
PAMI(43), No. 8, August 2021, pp. 2647-2664.
IEEE DOI 2107
BibRef
Earlier: A1, A4, A5, Only:
PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud,
CVPR19(770-779).
IEEE DOI 2002
Feature extraction, Proposals, Object detection, Convolution, autonomous driving BibRef

Mao, J.F.[Jia-Feng], Yu, Q.[Qing], Aizawa, K.[Kiyoharu],
Noisy Localization Annotation Refinement for Object Detection,
IEICE(E104-D), No. 9, September 2021, pp. 1478-1485.
WWW Link. 2109
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Earlier: ICIP20(2006-2010)
IEEE DOI 2011
Noise measurement, Training, Detectors, Object detection, Noise level, Task analysis, Robustness, joint optimization BibRef

Cao, J.[Jie], Ren, W.[Wei], Zhang, H.[Hong], Chen, Z.[Zuohan],
Candidate box fusion based approach to adjust position of the candidate box for object detection,
IET-IPR(15), No. 12, 2021, pp. 2799-2809.
DOI Link 2109
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Perreault, H.[Hughes], Bilodeau, G.A.[Guillaume-Alexandre], Saunier, N.[Nicolas], Héritier, M.[Maguelonne],
FFAVOD: Feature fusion architecture for video object detection,
PRL(151), 2021, pp. 294-301.
Elsevier DOI 2110
BibRef
Earlier:
SpotNet: Self-Attention Multi-Task Network for Object Detection,
CRV20(230-237)
IEEE DOI 2006
Video object detection, Feature fusion, Traffic scenes. Object Detection, Segmentation, Self-Attention, Multi-Task Learning, Traffic Scenes BibRef

Perreault, H.[Hughes], Heritier, M.[Maguelonne], Gravel, P.[Pierre], Bilodeau, G.A.[Guillaume-Alexandre], Saunier, N.[Nicolas],
RN-VID: A Feature Fusion Architecture for Video Object Detection,
ICIAR20(I:125-138).
Springer DOI 2007
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Iwayoshi, T.[Takaaki], Mitsuhara, M.[Masahiro], Takada, M.[Masayuki], Hirakawa, T.[Tsubasa], Yamashita, T.[Takayoshi], Fujiyoshi, H.[Hironobu],
Attention Mining Branch for Optimizing Attention Map,
MVA21(1-5)
DOI Link 2109
Attention branch networks work on region of interest, but object may not be there. Measurement, Training, Visualization, Target recognition, Dogs BibRef

He, Y.L.[Yu-Lin], Zhang, L.[Limeng], Chen, W.[Wei], Luo, X.[Xin], Jia, X.G.[Xiao-Gang], Li, C.[Chen],
CenterRepp: Predict Central Representative Point Set's Distribution For Detection,
ICPR21(8960-8967)
IEEE DOI 2105
Detectors, Object detection, Benchmark testing, Neck, Standards BibRef

Li, Y.L.[Yin-Lin], Qian, Y.[Yang], Yang, X.[Xu], Zhang, Y.[Yuren],
Activity and Relationship Modeling Driven Weakly Supervised Object Detection,
ICPR21(9628-9634)
IEEE DOI 2105
Training, Object detection, Gaussian distribution, Proposals BibRef

Adhikari, B.[Bishwo], Huttunen, H.[Heikki],
Iterative Bounding Box Annotation for Object Detection,
ICPR21(4040-4046)
IEEE DOI 2105
Training, Measurement, Annotations, Pipelines, Manuals, Object detection BibRef

Quan, Y.[Yu], Li, Z.X.[Zhi-Xin], Zhang, C.L.[Can-Long], Ma, H.F.[Hui-Fang],
Object Detection Model Based on Scene-Level Region Proposal Self-Attention,
ICPR21(954-961)
IEEE DOI 2105
Training, Analytical models, Visualization, Target recognition, Semantics, Object detection, Feature extraction, object detection, self-attention mechanism BibRef

Choi, M.K.[Min-Kook], Jung, H.[Heechul],
Development of Fast Refinement Detectors on AI Edge Platforms,
IML20(592-606).
Springer DOI 2103
Code, Object Detection.
WWW Link. Object detection on GPU BibRef

Xu, X.L.[Xiao-Long], Meng, F.M.[Fan-Man], Li, H.L.[Hong-Liang], Wu, Q.B.[Qing-Bo], Ngi Ngan, K.[King], Chen, S.[Shuai],
A New Bounding Box based Pseudo Annotation Generation Method for Semantic Segmentation,
VCIP20(100-103)
IEEE DOI 2102
Annotations, Image segmentation, Training, Semantics, Predictive models, Task analysis, Pipelines, Bounding Box, Class-agnostic Model BibRef

Duan, K.W.[Kai-Wen], Xie, L.X.[Ling-Xi], Qi, H.G.[Hong-Gang], Bai, S.[Song], Huang, Q.M.[Qing-Ming], Tian, Q.[Qi],
Corner Proposal Network for Anchor-free, Two-stage Object Detection,
ECCV20(III:399-416).
Springer DOI 2012
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Zhao, G.[Ganlong], Li, G.[Guanbin], Xu, R.[Ruijia], Lin, L.[Liang],
Collaborative Training Between Region Proposal Localization and Classification for Domain Adaptive Object Detection,
ECCV20(XVIII:86-102).
Springer DOI 2012
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Kim, K.[Kang], Lee, H.S.[Hee Seok],
Probabilistic Anchor Assignment with IoU Prediction for Object Detection,
ECCV20(XXV:355-371).
Springer DOI 2011
Intersection-over-Unions. BibRef

Chen, R.[Ran], Liu, Y.[Yong], Zhang, M.[Mengdan], Liu, S.[Shu], Yu, B.[Bei], Tai, Y.W.[Yu-Wing],
Dive Deeper into Box for Object Detection,
ECCV20(XXII:412-428).
Springer DOI 2011
BibRef

Xu, X., Luo, X., Ma, L.,
Context-Aware Hierarchical Feature Attention Network For Multi-Scale Object Detection,
ICIP20(2011-2015)
IEEE DOI 2011
Feature extraction, Detectors, Object detection, Context modeling, Semantics, Benchmark testing, Training, Object detection, Attention mechanism BibRef

Seo, G., Yoo, J., Cho, J., Kwak, N.,
Kl-Divergence-Based Region Proposal Network For Object Detection,
ICIP20(2001-2005)
IEEE DOI 2011
Uncertainty, Standards, Proposals, Training, Object detection, Gaussian distribution, Probability distribution, KL-Divergence BibRef

Chen, Q.[Qi], Sun, L.[Lin], Wang, Z.X.[Zhi-Xin], Jia, K.[Kui], Yuille, A.L.[Alan L.],
Object as Hotspots: An Anchor-free 3d Object Detection Approach via Firing of Hotspots,
ECCV20(XXI:68-84).
Springer DOI 2011
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Zhao, Z.[Zhen], Guo, Y.H.[Yu-Hong], Shen, H.F.[Hai-Feng], Ye, J.P.[Jie-Ping],
Adaptive Object Detection with Dual Multi-label Prediction,
ECCV20(XXVIII:54-69).
Springer DOI 2011
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Ma, W.S.[Wen-Shuo], Tian, T.Z.[Ting-Zhong], Xu, H.[Hang], Huang, Y.M.[Yi-Min], Li, Z.G.[Zhen-Guo],
AABO: Adaptive Anchor Box Optimization for Object Detection via Bayesian Sub-sampling,
ECCV20(V:560-575).
Springer DOI 2011
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Lan, S., Ren, Z., Wu, Y., Davis, L.S., Hua, G.,
SaccadeNet: A Fast and Accurate Object Detector,
CVPR20(10394-10403)
IEEE DOI 2008
Detectors, Training, Feature extraction, Object detection, Proposals, Aggregates BibRef

Qian, Q., Chen, L., Li, H., Jin, R.,
DR Loss: Improving Object Detection by Distributional Ranking,
CVPR20(12161-12169)
IEEE DOI 2008
Detectors, Object detection, Proposals, Neural networks, Task analysis, Feature extraction, Object recognition BibRef

Zhang, S., Chi, C., Yao, Y., Lei, Z., Li, S.Z.,
Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection,
CVPR20(9756-9765)
IEEE DOI 2008
Detectors, Training, Object detection, Proposals, Feature extraction, Computer vision, Bridges BibRef

Hosoya, Y., Suganuma, M., Okatani, T.,
Analysis and a Solution of Momentarily Missed Detection for Anchor-based Object Detectors,
WACV20(1399-1407)
IEEE DOI 2006
Detectors, Bicycles, Switches, Task analysis, Object detection, Employment, Clutter BibRef

Uzkent, B., Yeh, C., Ermon, S.,
Efficient Object Detection in Large Images Using Deep Reinforcement Learning,
WACV20(1813-1822)
IEEE DOI 2006
Detectors, Spatial resolution, Object detection, Proposals, Satellites BibRef

Chen, J., Luo, B., Wu, Q., Chen, J., Peng, X.,
Overlap Sampler for Region-Based Object Detection,
WACV20(756-764)
IEEE DOI 2006
Detectors, Training, Upper bound, Proposals, Object detection, Benchmark testing, Sampling methods BibRef

Dhamija, A.R., Günther, M., Ventura, J., Boult, T.E.,
The Overlooked Elephant of Object Detection: Open Set,
WACV20(1010-1019)
IEEE DOI 2006
Detectors, Object detection, Training, Protocols, Object recognition, Proposals, Training data BibRef

Gupta, D.[Dikshant], Anantharaman, A.[Aditya], Mamgain, N.[Nehal], Kamath, S.S.[S. Sowmya], Balasubramanian, V.N.[Vineeth N.], Jawahar, C.V.,
A Multi-Space Approach to Zero-Shot Object Detection,
WACV20(1198-1206)
IEEE DOI 2006
Semantics, Visualization, Object detection, Proposals, Task analysis, Training, Correlation BibRef

Li, Z., Du, X., Cao, Y.,
GAR: Graph Assisted Reasoning for Object Detection,
WACV20(1284-1293)
IEEE DOI 2006
Object detection, Proposals, Detectors, Image edge detection, Cognition, Task analysis, Cows BibRef

Zhong, Y., Wang, J., Peng, J., Zhang, L.,
Anchor Box Optimization for Object Detection,
WACV20(1275-1283)
IEEE DOI 2006
Shape, Training, Optimization, Object detection, Detectors, Robustness, Neural networks BibRef

Levinshtein, A., Sereshkeh, A.R., Derpanis, K.G.,
DATNet: Dense Auxiliary Tasks for Object Detection,
WACV20(1408-1416)
IEEE DOI 2006
Task analysis, Feature extraction, Semantics, Object detection, Proposals, Detectors, Transforms BibRef

Le, T., Akihiro, S., Ono, S., Kawasaki, H.,
Toward Interactive Self-Annotation For Video Object Bounding Box: Recurrent Self-Learning And Hierarchical Annotation Based Framework,
WACV20(3220-3229)
IEEE DOI 2006
Detectors, Labeling, Tools, Training data, Task analysis, Training, Machine learning BibRef

Tan, Z., Nie, X., Qian, Q., Li, N., Li, H.,
Learning to Rank Proposals for Object Detection,
ICCV19(8272-8280)
IEEE DOI 2004
edge detection, feature extraction, image fusion, learning (artificial intelligence), object detection BibRef

Yang, F., Fan, H., Chu, P., Blasch, E., Ling, H.,
Clustered Object Detection in Aerial Images,
ICCV19(8310-8319)
IEEE DOI 2004
estimation theory, feature extraction, image segmentation, object detection, pattern clustering, aerial images, Image resolution BibRef

Batchelor, O., Green, R.,
Object detection for Verification Based Annotation,
IVCNZ19(1-6)
IEEE DOI 2004
convolutional neural nets, image resolution, object detection, object detector, machine annotations, human annotator, human-in-the-loop BibRef

Chen, B.[Bo], Ghiasi, G.[Golnaz], Liu, H.X.[Han-Xiao], Lin, T.Y.[Tsung-Yi], Kalenichenko, D.[Dmitry], Adam, H.[Hartwig], Le, Q.V.[Quoc V.],
MnasFPN: Learning Latency-Aware Pyramid Architecture for Object Detection on Mobile Devices,
CVPR20(13604-13613)
IEEE DOI 2008
BibRef
Earlier: A2, A4, A7, Only:
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection,
CVPR19(7029-7038).
IEEE DOI 2002
Computer architecture, Head, Object detection, Search problems, Feature extraction, Mobile handsets, Aerospace electronics BibRef

Wang, J.[Jiaqi], Chen, K.[Kai], Yang, S.[Shuo], Loy, C.C.[Chen Change], Lin, D.[Dahua],
Region Proposal by Guided Anchoring,
CVPR19(2960-2969).
IEEE DOI 2002
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Singh, K.K.[Krishna Kumar], Lee, Y.J.[Yong Jae],
You Reap What You Sow: Using Videos to Generate High Precision Object Proposals for Weakly-Supervised Object Detection,
CVPR19(9406-9414).
IEEE DOI 2002
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Rezatofighi, H.[Hamid], Tsoi, N.[Nathan], Gwak, J.[JunYoung], Sadeghian, A.[Amir], Reid, I.D.[Ian D.], Savarese, S.[Silvio],
Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression,
CVPR19(658-666).
IEEE DOI 2002
BibRef

He, Y.H.[Yi-Hui], Zhu, C.C.[Chen-Chen], Wang, J.R.[Jian-Ren], Savvides, M.[Marios], Zhang, X.Y.[Xiang-Yu],
Bounding Box Regression With Uncertainty for Accurate Object Detection,
CVPR19(2883-2892).
IEEE DOI 2002
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Lee, W.[Wonhee], Na, J.[Joonil], Kim, G.[Gunhee],
Multi-Task Self-Supervised Object Detection via Recycling of Bounding Box Annotations,
CVPR19(4979-4988).
IEEE DOI 2002
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Ribera, J.[Javier], Guera, D.[David], Chen, Y.[Yuhao], Delp, E.J.[Edward J.],
Locating Objects Without Bounding Boxes,
CVPR19(6472-6482).
IEEE DOI 2002
BibRef

Cho, M., Chung, T., Lee, H., Lee, S.,
N-RPN: Hard Example Learning For Region Proposal Networks,
ICIP19(3955-3959)
IEEE DOI 1910
Region proposal, hard negative example learning, hard example mining, object detection 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.K.[Seung-Kwan], Kwak, S.[Suha], Cho, M.[Minsu],
Universal Bounding Box Regression and Its Applications,
ACCV18(VI:373-387).
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.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

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
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Teh, E.W.[Eu Wern], Rochan, M.[Mrigank], Wang, Y.[Yang],
Attention Networks for Weakly Supervised Object Localization,
BMVC16(xx-yy).
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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

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, 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, 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

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

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