8.6.1 Instance Segmentation

Chapter Contents (Back)
Segmentation, Guided. Segmentation, Instance. Instance Segmentation. Count Objects.
See also Panoptic Segmentation.

Harwood, D.A., Chang, S., and Davis, L.S.,
Interpreting Aerial Photographs by Segmentation and Search,
DARPA87(507-520). (
See also Sigma Image Understanding System, The. ) Find segments (homogeneous regions), then find instances which satisfy definitions of object types, then search for support, then improve instances, then iterate with new estimates of parameters.
See also Fua and Leclerc Guided Segmentation Papers. BibRef 8700

Meng, J., Yuan, J., Yang, J., Wang, G., Tan, Y.P.,
Object Instance Search in Videos via Spatio-Temporal Trajectory Discovery,
MultMed(18), No. 1, January 2016, pp. 116-127.
IEEE DOI 1601
Find specific object. BibRef

Wang, L.T.[Lian-Tao], Meng, D., Hu, X.L.[Xue-Lei], Lu, J.F.[Jian-Feng], Zhao, J.[Ji],
Instance Annotation via Optimal BoW for Weakly Supervised Object Localization,
Cyber(47), No. 5, May 2017, pp. 1313-1324.
IEEE DOI 1704
BibRef
Earlier: A1, A5, A3, A4, Only:
Weakly supervised object localization via maximal entropy randomwalk,
ICIP14(1614-1617)
IEEE DOI 1502
Birds. Entropy BibRef

Yu, J.G.[Jin-Gang], Li, Y.S.[Yan-Sheng], Gao, C.X.[Chang-Xin], Gao, H.X.[Hong-Xia], Xia, G.S.[Gui-Song], Yu, Z.L.[Zhu Liang], Li, Y.Q.[Yuan-Qing],
Exemplar-Based Recursive Instance Segmentation With Application to Plant Image Analysis,
IP(29), No. 1, 2020, pp. 389-404.
IEEE DOI 1910
biology computing, computational complexity, computer vision, image classification, image segmentation, inference mechanisms, plant phenotyping BibRef

Zhang, H., Tian, Y., Wang, K., Zhang, W., Wang, F.,
Mask SSD: An Effective Single-Stage Approach to Object Instance Segmentation,
IP(29), 2020, pp. 2078-2093.
IEEE DOI 2001
Object detection, instance segmentation, feedback features, single-shot detector BibRef

Goldman, E.[Eran], Goldberger, J.[Jacob],
CRF with deep class embedding for large scale classification,
CVIU(191), 2020, pp. 102865.
Elsevier DOI 2002
CRF, Class embedding, Matrix factorization, Surrogate likelihood, Batch normalization BibRef

Goldman, E.[Eran], Herzig, R.[Roei], Eisenschtat, A.[Aviv], Goldberger, J.[Jacob], Hassner, T.[Tal],
Precise Detection in Densely Packed Scenes,
CVPR19(5222-5231).
IEEE DOI 2002
E.g. man-made scenes with numerous identical objects. BibRef

Su, H.[Hao], Wei, S.J.[Shun-Jun], Liu, S.[Shan], Liang, J.D.[Jia-Dian], Wang, C.[Chen], Shi, J.[Jun], Zhang, X.L.[Xiao-Ling],
HQ-ISNet: High-Quality Instance Segmentation for Remote Sensing Imagery,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Grard, M.[Matthieu], Dellandréa, E.[Emmanuel], Chen, L.M.[Li-Ming],
Deep Multicameral Decoding for Localizing Unoccluded Object Instances from a Single RGB Image,
IJCV(128), No. 5, May 2020, pp. 1331-1359.
Springer DOI 2005
BibRef

Zhang, S.H.[Shi-Hui], Li, H.[He], Kong, W.H.[Wei-Hang],
Object counting method based on dual attention network,
IET-IPR(14), No. 8, 19 June 2020, pp. 1621-1627.
DOI Link 2005
BibRef

Oba, T.[Takeru], Ukita, N.[Norimichi],
Instance Segmentation by Semi-Supervised Learning and Image Synthesis,
IEICE(E103-D), No. 6, June 2020, pp. 1247-1256.
WWW Link. 2006
BibRef

Li, H.[He], Zhang, S.H.[Shi-Hui], Kong, W.H.[Wei-Hang],
Bilateral counting network for single-image object counting,
VC(36), No. 8, August 2020, pp. 1693-1704.
WWW Link. 2007
BibRef

Hafiz, A.M.[Abdul Mueed], Bhat, G.M.[Ghulam Mohiuddin],
A survey on instance segmentation: state of the art,
MultInfoRetr(9), No. 3, September 2020, pp. 171-189.
WWW Link. 2008
BibRef

Liu, L., Lu, H., Xiong, H., Xian, K., Cao, Z., Shen, C.,
Counting Objects by Blockwise Classification,
CirSysVideo(30), No. 10, October 2020, pp. 3513-3527.
IEEE DOI 2010
Kernel, Nonhomogeneous media, Task analysis, Feature extraction, Quantization (signal), Convolutional neural networks, count-level classification BibRef

Kim, N.[Nuri], Lee, D.[Donghoon], Oh, S.H.[Song-Hwai],
Learning instance-aware object detection using determinantal point processes,
CVIU(201), 2020, pp. 103061.
Elsevier DOI 2011
Determinantal Point Processes, Object Detection, Crowd Detection BibRef

Hu, Z.[Zheng], Liu, Z.[Zhi], Li, G.[Gongyang], Ye, L.W.[Lin-Wei], Zhou, L.[Lei], Wang, Y.[Yang],
Weakly supervised instance segmentation using multi-stage erasing refinement and saliency-guided proposals ordering,
JVCIR(73), 2020, pp. 102957.
Elsevier DOI 2012
Weakly supervised instance segmentation, Image-level annotations, Multi-stage erasing refinement, Saliency-guided proposals ordering BibRef

Feng, D.[Dong], Liang, M.G.[Man-Gui], Gao, F.[Feng], Huang, Y.C.[Yi-Cheng], Zhang, X.F.[Xin-Feng], Duan, L.Y.[Ling-Yu],
Towards Large-Scale Object Instance Search: A Multi-Block N-Ary Trie,
CirSysVideo(31), No. 1, January 2021, pp. 372-386.
IEEE DOI 2101
Veins, Binary codes, Search problems, Computational efficiency, Task analysis, Optimization, Indexing, Object search, binary code, approximate nearest neighbor (ANN) search BibRef

Ferreira de Carvalho, O.L.[Osmar Luiz], de Carvalho Júnior, O.A.[Osmar Abílio], de Albuquerque, A.O.[Anesmar Olino], de Bem, P.P.[Pablo Pozzobon], Silva, C.R.[Cristiano Rosa], Ferreira, P.H.G.[Pedro Henrique Guimarães], dos Santos de Moura, R.[Rebeca], Gomes, R.A.T.[Roberto Arnaldo Trancoso], Guimarães, R.F.[Renato Fontes], Borges, D.L.[Díbio Leandro],
Instance Segmentation for Large, Multi-Channel Remote Sensing Imagery Using Mask-RCNN and a Mosaicking Approach,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Liu, D., Zhang, D., Song, Y., Huang, H., Cai, W.,
Panoptic Feature Fusion Net: A Novel Instance Segmentation Paradigm for Biomedical and Biological Images,
IP(30), 2021, pp. 2045-2059.
IEEE DOI 2101
Semantics, Image segmentation, Task analysis, Biology, Biomedical imaging, Computer architecture, Histopathology, plant phenotype images BibRef

Chen, L.W.[Lin-Wei], Fu, Y.[Ying], You, S.[Shaodi], Liu, H.Z.[Hong-Zhe],
Efficient Hybrid Supervision for Instance Segmentation in Aerial Images,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Gao, N.Y.[Nai-Yu], Shan, Y.H.[Yan-Hu], Wang, Y.P.[Yu-Pei], Zhao, X.[Xin], Huang, K.Q.[Kai-Qi],
SSAP: Single-Shot Instance Segmentation With Affinity Pyramid,
CirSysVideo(31), No. 2, February 2021, pp. 661-673.
IEEE DOI 2102
Semantics, Image segmentation, Training, Proposals, Task analysis, Automation, Predictive models, Instance segmentation, graph partition BibRef

Gao, N.Y.[Nai-Yu], Shan, Y.H.[Yan-Hu], Wang, Y.P.[Yu-Pei], Zhao, X.[Xin], Yu, Y.N.[Yi-Nan], Yang, M.[Ming], Huang, K.Q.[Kai-Qi],
SSAP: Single-Shot Instance Segmentation With Affinity Pyramid,
ICCV19(642-651)
IEEE DOI 2004
graph theory, image segmentation, learning (artificial intelligence), Acceleration BibRef

Xu, Y., Zhou, C., Yu, X., Xiao, B., Yang, Y.,
Pyramidal Multiple Instance Detection Network With Mask Guided Self-Correction for Weakly Supervised Object Detection,
IP(30), 2021, pp. 3029-3040.
IEEE DOI 2102
Proposals, Annotations, Object detection, Training, Image segmentation, Detectors, Task analysis, pyramidal network BibRef

Yu, J., Yao, J., Zhang, J., Yu, Z., Tao, D.,
SPRNet: Single-Pixel Reconstruction for One-Stage Instance Segmentation,
Cyber(51), No. 4, April 2021, pp. 1731-1742.
IEEE DOI 2103
Semantics, Detectors, Image segmentation, Object detection, Task analysis, Proposals, Image reconstruction, Computer vision, video analyze BibRef

Wu, Y.H., Liu, Y., Zhang, L., Gao, W., Cheng, M.M.,
Regularized Densely-Connected Pyramid Network for Salient Instance Segmentation,
IP(30), 2021, pp. 3897-3907.
IEEE DOI 2104
Feature extraction, Semantics, Image segmentation, Visualization, Task analysis, Object detection, Convolution, RoIAlign BibRef

Zhu, L.C.[Lin-Chao], Fan, H.H.[He-He], Luo, Y.W.[Ya-Wei], Xu, M.L.[Ming-Liang], Yang, Y.[Yi],
Few-Shot Common-Object Reasoning Using Common-Centric Localization Network,
IP(30), 2021, pp. 4253-4262.
IEEE DOI 2104
Feature extraction, Object detection, Task analysis, Location awareness, Annotations, Cognition, Proposals, object detection BibRef

Wang, H.[Hui], Li, H.[Hao], Qian, W.L.[Wan-Li], Diao, W.H.[Wen-Hui], Zhao, L.J.[Liang-Jin], Zhang, J.H.[Jing-Hua], Zhang, D.B.[Dao-Bing],
Dynamic Pseudo-Label Generation for Weakly Supervised Object Detection in Remote Sensing Images,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Fu, K.[Kun], Chang, Z.H.[Zhong-Han], Zhang, Y.[Yue], Sun, X.[Xian],
Point-Based Estimator for Arbitrary-Oriented Object Detection in Aerial Images,
GeoRS(59), No. 5, May 2021, pp. 4370-4387.
IEEE DOI 2104
Object detection, Detectors, Task analysis, Feature extraction, Predictive models, Object recognition, Quantization (signal), point-based estimator BibRef

Lu, J.Y.[Jun-Yan], Jia, H.G.[Hong-Guang], Li, T.[Tie], Li, Z.Q.[Zhu-Qiang], Ma, J.Y.[Jing-Yu], Zhu, R.F.[Rui-Fei],
An Instance Segmentation Based Framework for Large-Sized High-Resolution Remote Sensing Images Registration,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Tan, J.[Jingang], Wang, K.[Kangru], Chen, L.[Lili], Zhang, G.H.[Guang-Hui], Li, J.[Jiamao], Zhang, X.L.[Xiao-Lin],
HCFS3D: Hierarchical coupled feature selection network for 3D semantic and instance segmentation,
IVC(109), 2021, pp. 104129.
Elsevier DOI 2105
Point clouds, Semantic segmentation, Instance segmentation, Feature selection, Mutual assistance, Conditional random fields BibRef

Pang, J.M.[Jiang-Miao], Chen, K.[Kai], Li, Q.[Qi], Xu, Z.H.[Zhi-Hai], Feng, H.J.[Hua-Jun], Shi, J.P.[Jian-Ping], Ouyang, W.L.[Wan-Li], Lin, D.H.[Da-Hua],
Towards Balanced Learning for Instance Recognition,
IJCV(129), No. 5, May 2021, pp. 1376-1393.
Springer DOI 2105
BibRef

Sung, P.W.[Po-Wei], Yang, W.J.[Wei-Jong], Yang, J.F.[Jar-Ferr], Chan, D.Y.[Din-Yuan],
An interactive instance segmentation system with multi-resolution convolutional neural networks,
IET-CV(15), No. 2, 2021, pp. 99-109.
DOI Link 2106
BibRef

Liu, N.[Nian], Zhao, W.B.[Wang-Bo], Shao, L.[Ling], Han, J.W.[Jun-Wei],
SCG: Saliency and Contour Guided Salient Instance Segmentation,
IP(30), 2021, pp. 5862-5874.
IEEE DOI 2107
Task analysis, Saliency detection, Image segmentation, Head, Proposals, Feature extraction, Computational modeling, attention model BibRef

Gao, N.[Naiyu], Shan, Y.[Yanhu], Zhao, X.[Xin], Huang, K.Q.[Kai-Qi],
Learning Category- and Instance-Aware Pixel Embedding for Fast Panoptic Segmentation,
IP(30), 2021, pp. 6013-6023.
IEEE DOI 2107
Semantic and instance together. Image segmentation, Semantics, Predictive models, Task analysis, Pipelines, Image color analysis, Head, Panoptic segmentation, pixel embedding BibRef

Wu, Z.T.[Zi-Tong], Hou, B.[Biao], Ren, B.[Bo], Ren, Z.L.[Zhong-Le], Wang, S.[Shuang], Jiao, L.C.[Li-Cheng],
A Deep Detection Network Based on Interaction of Instance Segmentation and Object Detection for SAR Images,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Tan, J.[Jingang], Chen, L.[Lili], Wang, K.R.[Kang-Ru], Li, J.M.[Jia-Mao], Zhang, X.L.[Xiao-Lin],
SASO: Joint 3D semantic-instance segmentation via multi-scale semantic association and salient point clustering optimization,
IET-CV(15), No. 5, 2021, pp. 366-379.
DOI Link 2107
BibRef

Xu, C.[Can], Yuen, P.[Peter], Lang, W.X.[Wen-Xi], Xin, R.[Rui], Mao, K.[Kaichen], Jiang, H.Y.[Hai-Yan],
Generative detect for occlusion object based on occlusion generation and feature completing,
JVCIR(78), 2021, pp. 103189.
Elsevier DOI 2107
Apply it to the in-filed Rice Panicles Counting. Occlusion, Object detection, Feature completing, Generative adversarial networks BibRef

Zeng, X.F.[Xiang-Feng], Wei, S.J.[Shun-Jun], Wei, J.S.[Jin-Shan], Zhou, Z.C.[Zi-Chen], Shi, J.[Jun], Zhang, X.L.[Xiao-Ling], Fan, F.[Fan],
CPISNet: Delving into Consistent Proposals of Instance Segmentation Network for High-Resolution Aerial Images,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Xu, W.[Wei], Liang, D.[Dingkang], Zheng, Y.[Yixiao], Xie, J.[Jiahao], Ma, Z.[Zhanyu],
Dilated-Scale-Aware Category-Attention ConvNet for Multi-Class Object Counting,
SPLetters(28), 2021, pp. 1570-1574.
IEEE DOI 2108
Annotations, Feature extraction, Task analysis, Convolution, Automobiles, Training, Visualization, Multi-class object counting, category-attention module BibRef

Yi, J.R.[Jing-Ru], Wu, P.X.[Peng-Xiang], Tang, H.[Hui], Liu, B.[Bo], Huang, Q.[Qiaoying], Qu, H.[Hui], Han, L.[Lianyi], Fan, W.[Wei], Hoeppner, D.J.[Daniel J.], Metaxas, D.N.[Dimitris N.],
Object-Guided Instance Segmentation With Auxiliary Feature Refinement for Biological Images,
MedImg(40), No. 9, September 2021, pp. 2403-2414.
IEEE DOI 2109
Image segmentation, Heating systems, Head, Feature extraction, Detectors, Object detection, Shape, Instance segmentation, medical image segmentation BibRef

Zhan, Y.[Yu], Zhao, W.L.[Wan-Lei],
Instance search via instance level segmentation and feature representation,
JVCIR(79), 2021, pp. 103253.
Elsevier DOI 2109
Instance search, Instance segmentation, CNN BibRef

Gominski, D.[Dimitri], Gouet-Brunet, V.[Valérie], Chen, L.M.[Li-Ming],
Connecting Images through Sources: Exploring Low-Data, Heterogeneous Instance Retrieval,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Karara, G.[Ghizlane], Hajji, R.[Rafika], Poux, F.[Florent],
3D Point Cloud Semantic Augmentation: Instance Segmentation of 360° Panoramas by Deep Learning Techniques,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Hao, S.Y.[Sheng-Yu], Wang, G.[Gaoang], Gu, R.S.[Ren-Shu],
Weakly supervised instance segmentation using multi-prior fusion,
CVIU(211), 2021, pp. 103261.
Elsevier DOI 2110
Instance segmentation, Weakly supervised, Multi-priors, Bounding box annotations BibRef


Kim, M.[Myungchul], Woo, S.[Sanghyun], Kim, D.[Dahun], Kweon, I.S.[In So],
The Devil is in the Boundary: Exploiting Boundary Representation for Basis-based Instance Segmentation,
WACV21(928-937)
IEEE DOI 2106
Measurement, Image segmentation, Image resolution, Shape, Predictive models BibRef

Mercier, J.P.[Jean-Philippe], Garon, M.[Mathieu], Giguère, P.[Philippe], Lalonde, J.F.[Jean-François],
Deep Template-based Object Instance Detection,
WACV21(1506-1515)
IEEE DOI 2106
Training, Matched filters, Service robots, Toy manufacturing industry, Object detection BibRef

Hwang, J.[Jaedong], Kim, S.[Seohyun], Son, J.[Jeany], Han, B.H.[Bo-Hyung],
Weakly Supervised Instance Segmentation by Deep Community Learning,
WACV21(1019-1028)
IEEE DOI 2106
Training, Neural networks, Semantics, Object detection, Detectors, Feature extraction, Proposals BibRef

Goel, K.[Kratarth], Srinivasan, P.[Praveen], Tariq, S.[Sarah], Philbin, J.[James],
QuadroNet: Multi-Task Learning for Real-Time Semantic Depth Aware Instance Segmentation,
WACV21(315-324)
IEEE DOI 2106
Training, Image segmentation, Laser radar, Semantics, Object detection, Network architecture BibRef

Liu, Z.C.[Zi-Chen], Liew, J.H.[Jun Hao], Chen, X.Y.[Xiang-Yu], Feng, J.[Jiashi],
DANCE: A Deep Attentive Contour Model for Efficient Instance Segmentation,
WACV21(345-354)
IEEE DOI 2106
Deformable models, Training, Computational modeling, Pipelines, Real-time systems BibRef

Yang, S.D.[Shuo-Diao], Su, H.T.[Hung-Ting], Hsu, W.H.[Winston H.], Chen, W.C.[Wen-Chin],
Class-agnostic Few-shot Object Counting,
WACV21(869-877)
IEEE DOI 2106
Training, Computational modeling, Force, Data collection, Data models BibRef

Liu, X.L.[Xiao-Long], Hou, Y.Q.[Yu-Qing], Yao, A.[Anbang], Chen, Y.R.[Yu-Rong], Li, K.Q.[Ke-Qiang],
CASNet: Common Attribute Support Network for image instance and panoptic segmentation,
ICPR21(8469-8475)
IEEE DOI 2105
Training, Bridges, Image segmentation, Semantics, Clustering algorithms, Object detection, Prediction algorithms BibRef

Deng, Z.L.[Zhao-Li], Yang, C.[Chenhui],
Multiple-step Sampling for Dense Object Detection and Counting,
ICPR21(1036-1042)
IEEE DOI 2105
Training, Detectors, Object detection, Benchmark testing, Sampling methods, Feature extraction, Pattern recognition, object counting BibRef

Godi, M.[Marco], Joppi, C.[Christian], Giachetti, A.[Andrea], Cristani, M.[Marco],
SIMCO: SIMilarity-based object COunting,
ICPR21(47-52)
IEEE DOI 2105
Training, Head, Shape, Image color analysis, Annotations, Benchmark testing, Pattern recognition BibRef

Rossi, L.[Leonardo], Karimi, A.[Akbar], Prati, A.[Andrea],
A Novel Region of Interest Extraction Layer for Instance Segmentation,
ICPR21(2203-2209)
IEEE DOI 2105
Architecture, Neural networks, Computer architecture, Object detection, Feature extraction, Pattern recognition BibRef

Li, X.R.[Xi-Rong], Wan, W.C.[Wen-Cui], Zhou, Y.[Yang], Zhao, J.C.[Jian-Chun], Wei, Q.J.[Qi-Jie], Rong, J.[Junbo], Zhou, P.Y.[Peng-Yi], Xu, L.M.[Li-Min], Lang, L.[Lijuan], Liu, Y.Y.[Yu-Ying], Niu, C.Z.[Cheng-Zhi], Ding, D.[Dayong], Jin, X.M.[Xue-Min],
Deep Multiple Instance Learning with Spatial Attention for ROP Case Classification, Instance Selection and Abnormality Localization,
ICPR21(7293-7298)
IEEE DOI 2105
Location awareness, Visualization, Retinopathy, Image color analysis, Retina, Pattern recognition, Task analysis, abnormality localization BibRef

Riaz, H.U.M.[Hamd Ul Moqeet], Benbarka, N.[Nuri], Zell, A.[Andreas],
FourierNet: Compact Mask Representation for Instance Segmentation Using Differentiable Shape Decoders,
ICPR21(7833-7840)
IEEE DOI 2105
Training, Image resolution, Shape, Transforms, Detectors, Real-time systems, Decoding, Instance segmentation, Fourier series, differentiable algorithms BibRef

Heidecker, F.[Florian], Hannan, A.[Abdul], Bieshaar, M.[Maarten], Sick, B.[Bernhard],
Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks,
HCAU20(361-374).
Springer DOI 2103
BibRef

Schneegans, J.[Jan], Bieshaar, M.[Maarten], Heidecker, F.[Florian], Sick, B.[Bernhard],
Intelligent and Interactive Video Annotation for Instance Segmentation Using Siamese Neural Networks,
HCAU20(375-389).
Springer DOI 2103
BibRef

Ito, S.[Satoshi], Kubota, S.[Susumu],
Point Proposal Based Instance Segmentation with Rectangular Masks for Robot Picking Task,
ACCV20(III:641-653).
Springer DOI 2103
BibRef

Yang, L., Li, H., Wu, Q., Meng, F., Ngi Ngan, K.,
Mono is Enough: Instance Segmentation from Single Annotated Sample,
VCIP20(120-123)
IEEE DOI 2102
Image segmentation, Distortion, Brightness, Data models, Annotations, Training data, Task analysis, Instance Segmentation, Data Augmentation BibRef

Wang, X.L.[Xin-Long], Kong, T.[Tao], Shen, C.H.[Chun-Hua], Jiang, Y.N.[Yu-Ning], Li, L.[Lei],
SOLO: Segmenting Objects by Locations,
ECCV20(XVIII:649-665).
Springer DOI 2012
Code, Segmentation.
WWW Link. BibRef

Mais, L.[Lisa], Hirsch, P.[Peter], Kainmueller, D.[Dagmar],
PatchPerPix for Instance Segmentation,
ECCV20(XXV:288-304).
Springer DOI 2011
BibRef

Chen, X.[Xier], Lian, Y.C.[Yan-Chao], Jiao, L.C.[Li-Cheng], Wang, H.R.[Hao-Ran], Gao, Y.J.[Yan-Jie], Shi, L.L.[Ling-Ling],
Supervised Edge Attention Network for Accurate Image Instance Segmentation,
ECCV20(XXVII:617-631).
Springer DOI 2011
BibRef

Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.,
Proposal-Based Instance Segmentation With Point Supervision,
ICIP20(2126-2130)
IEEE DOI 2011
Proposals, Training, Image segmentation, Task analysis, Predictive models, Automobiles, Autonomous vehicles, weak supervision BibRef

Laradji, I.H., Pardinas, R., Rodriguez, P., Vazquez, D.,
LOOC: Localize Overlapping Objects with Count Supervision,
ICIP20(2316-2320)
IEEE DOI 2011
Proposals, Training, Games, Task analysis, Object recognition, Generators, Videos, localization, counting, weakly supervised BibRef

Cheng, T.H.[Tian-Heng], Wang, X.G.[Xing-Gang], Huang, L.[Lichao], Liu, W.Y.[Wen-Yu],
Boundary-preserving Mask R-CNN,
ECCV20(XIV:660-676).
Springer DOI 2011
BibRef

Cao, J.[Jiale], Anwer, R.M.[Rao Muhammad], Cholakkal, H.[Hisham], Khan, F.S.[Fahad Shahbaz], Pang, Y.W.[Yan-Wei], Shao, L.[Ling],
Sipmask: Spatial Information Preservation for Fast Image and Video Instance Segmentation,
ECCV20(XIV:1-18).
Springer DOI 2011
BibRef

Yang, L.R.[Long-Rong], Meng, F.[Fanman], Li, H.L.[Hong-Liang], Wu, Q.B.[Qing-Bo], Cheng, Q.S.[Qi-Shang],
Learning with Noisy Class Labels for Instance Segmentation,
ECCV20(XIV:38-53).
Springer DOI 2011
BibRef

Wang, T.[Tao], Li, Y.[Yu], Kang, B.Y.[Bing-Yi], Li, J.[Junnan], Liew, J.[Junhao], Tang, S.[Sheng], Feng, S.H.[Steven HoiJiashi],
The Devil Is in Classification: A Simple Framework for Long-tail Instance Segmentation,
ECCV20(XIV:728-744).
Springer DOI 2011
BibRef

Fan, Q.[Qi], Ke, L.[Lei], Pei, W.J.[Wen-Jie], Tang, C.K.[Chi-Keung], Tai, Y.W.[Yu-Wing],
Commonality-parsing Network Across Shape and Appearance for Partially Supervised Instance Segmentation,
ECCV20(VIII:379-396).
Springer DOI 2011
BibRef

Wei, F.Y.[Fang-Yun], Sun, X.[Xiao], Li, H.Y.[Hong-Yang], Wang, J.D.[Jing-Dong], Lin, S.[Stephen],
Point-set Anchors for Object Detection, Instance Segmentation and Pose Estimation,
ECCV20(X:527-544).
Springer DOI 2011
BibRef

Arun, A.[Aditya], Jawahar, C.V., Kumar, M.P.[M. Pawan],
Weakly Supervised Instance Segmentation by Learning Annotation Consistent Instances,
ECCV20(XXVIII:254-270).
Springer DOI 2011
BibRef

Homayounfar, N.[Namdar], Xiong, Y.[Yuwen], Liang, J.[Justin], Ma, W.C.[Wei-Chiu], Urtasun, R.[Raquel],
Levelset R-CNN: A Deep Variational Method for Instance Segmentation,
ECCV20(XXIII:555-571).
Springer DOI 2011
BibRef

Tian, Z.[Zhi], Shen, C.H.[Chun-Hua], Chen, H.[Hao],
Conditional Convolutions for Instance Segmentation,
ECCV20(I:282-298).
Springer DOI 2011
BibRef

Veksler, O.[Olga],
Regularized Loss for Weakly Supervised Single Class Semantic Segmentation,
ECCV20(XXIX: 348-365).
Springer DOI 2010
BibRef

Zhou, Y., Wang, X., Jiao, J., Darrell, T.J., Yu, F.,
Learning Saliency Propagation for Semi-Supervised Instance Segmentation,
CVPR20(10304-10313)
IEEE DOI 2008
Shape, Image segmentation, Head, Feature extraction, Task analysis, Semantics, Message passing BibRef

Cao, J., Cholakkal, H., Anwer, R.M., Khan, F.S., Pang, Y., Shao, L.,
D2Det: Towards High Quality Object Detection and Instance Segmentation,
CVPR20(11482-11491)
IEEE DOI 2008
Proposals, Detectors, Object detection, Feature extraction, Standards, Training, Benchmark testing BibRef

Zeni, L.F., Jung, C.R.,
Distilling Knowledge from Refinement in Multiple Instance Detection Networks,
DeepVision20(3324-3333)
IEEE DOI 2008
Proposals, Feature extraction, Training, Detectors, Object detection, Knowledge engineering, Task analysis BibRef

Jiang, L., Zhao, H., Shi, S., Liu, S., Fu, C., Jia, J.,
PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation,
CVPR20(4866-4875)
IEEE DOI 2008
Semantics, Feature extraction, Task analysis, Space exploration, Proposals BibRef

Peng, S., Jiang, W., Pi, H., Li, X., Bao, H., Zhou, X.,
Deep Snake for Real-Time Instance Segmentation,
CVPR20(8530-8539)
IEEE DOI 2008
Convolution, Image segmentation, Standards, Pipelines, Kernel, Strain, Real-time systems BibRef

Chen, H., Sun, K., Tian, Z., Shen, C., Huang, Y., Yan, Y.,
BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation,
CVPR20(8570-8578)
IEEE DOI 2008
Agriculture, Shape, Convolution, Detectors, Proposals, Task analysis, Computational complexity BibRef

Liang, J., Homayounfar, N., Ma, W., Xiong, Y., Hu, R., Urtasun, R.,
PolyTransform: Deep Polygon Transformer for Instance Segmentation,
CVPR20(9128-9137)
IEEE DOI 2008
Feature extraction, Task analysis, Image segmentation, Proposals, Semantics, Computational modeling, Measurement BibRef

Fan, Z., Yu, J., Liang, Z., Ou, J., Gao, C., Xia, G., Li, Y.,
FGN: Fully Guided Network for Few-Shot Instance Segmentation,
CVPR20(9169-9178)
IEEE DOI 2008
Task analysis, Image segmentation, Semantics, Training, Detectors, Proposals, Adaptation models BibRef

Wang, Y.Q.[Yu-Qing], Xu, Z.L.[Zhao-Liang], Shen, H.[Hao], Cheng, B.S.[Bao-Shan], Yang, L.R.[Li-Rong],
CenterMask: Single Shot Instance Segmentation With Point Representation,
CVPR20(9310-9318)
IEEE DOI 2008
Shape, Image segmentation, Head, Feature extraction, Detectors, Visualization BibRef

Zhang, R., Tian, Z., Shen, C., You, M., Yan, Y.,
Mask Encoding for Single Shot Instance Segmentation,
CVPR20(10223-10232)
IEEE DOI 2008
Encoding, Task analysis, Detectors, Feature extraction, Pipelines, Principal component analysis, Training BibRef

Xie, E., Sun, P., Song, X., Wang, W., Liu, X., Liang, D., Shen, C., Luo, P.,
PolarMask: Single Shot Instance Segmentation With Polar Representation,
CVPR20(12190-12199)
IEEE DOI 2008
Image segmentation, Task analysis, Training, Detectors, Complexity theory, Pipelines, Feature extraction BibRef

Jiang, H., Yan, F., Cai, J., Zheng, J., Xiao, J.,
End-to-End 3D Point Cloud Instance Segmentation Without Detection,
CVPR20(12793-12802)
IEEE DOI 2008
Semantics, Feature extraction, Training, Task analysis, Clustering algorithms BibRef

Lin, C., Hung, Y., Feris, R., He, L.,
Video Instance Segmentation Tracking With a Modified VAE Architecture,
CVPR20(13144-13154)
IEEE DOI 2008
Task analysis, Decoding, Proposals, Motion segmentation, Object segmentation, Target tracking, Image segmentation BibRef

Lee, Y., Park, J.,
CenterMask: Real-Time Anchor-Free Instance Segmentation,
CVPR20(13903-13912)
IEEE DOI 2008
Detectors, Feature extraction, Real-time systems, Head, Object detection, Proposals, Computer architecture BibRef

Zhou, D., Fang, J., Song, X., Liu, L., Yin, J., Dai, Y., Li, H., Yang, R.,
Joint 3D Instance Segmentation and Object Detection for Autonomous Driving,
CVPR20(1836-1846)
IEEE DOI 2008
Object detection, Proposals, Feature extraction, Shape, Semantics BibRef

Han, L., Zheng, T., Xu, L., Fang, L.,
OccuSeg: Occupancy-Aware 3D Instance Segmentation,
CVPR20(2937-2946)
IEEE DOI 2008
Image segmentation, Feature extraction, Semantics, Proposals, Solid modeling BibRef

Engelmann, F., Bokeloh, M., Fathi, A., Leibe, B., Nießner, M.,
3D-MPA: Multi-Proposal Aggregation for 3D Semantic Instance Segmentation,
CVPR20(9028-9037)
IEEE DOI 2008
Proposals, Semantics, Object detection, Computer vision, Geometry BibRef

Chu, X., Zheng, A., Zhang, X., Sun, J.,
Detection in Crowded Scenes: One Proposal, Multiple Predictions,
CVPR20(12211-12220)
IEEE DOI 2008
Proposals, Detectors, Object detection, Color, Pipelines, Neural networks, Computer vision BibRef

Yang, Y., Li, G., Wu, Z., Su, L., Huang, Q., Sebe, N.,
Reverse Perspective Network for Perspective-Aware Object Counting,
CVPR20(4373-4382)
IEEE DOI 2008
Distortion, Training, Feature extraction, Estimation, Convolution, Kernel, Adaptation models BibRef

Wang, Q.[Qiong], Zhang, L.[Lu], Kpalma, K.[Kidiyo],
A Semantics-guided Warping for Semi-supervised Video Object Instance Segmentation,
ICIAR20(I:186-195).
Springer DOI 2007
BibRef

Ma, J.[Jin], Pang, S.M.[Shan-Min], Yang, B.[Bo], Zhu, J.H.[Ji-Hua], Li, Y.C.[Yao-Chen],
Spatial-Content Image Search in Complex Scenes,
WACV20(2492-2500)
IEEE DOI 2006
Code, Image Search.
WWW Link. Visualization, Semantics, Image retrieval, Feature extraction, Image representation, Object detection BibRef

Sharma, K., Gold, M., Zurbruegg, C., Leal-Taixé, L., Wegner, J.D.,
HistoNet: Predicting size histograms of object instances,
WACV20(3626-3634)
IEEE DOI 2006
Histograms, Image segmentation, Task analysis, Estimation, Computer architecture, Training, Cancer BibRef

Royer, A., Lampert, C.H.,
Localizing Grouped Instances for Efficient Detection in Low-Resource Scenarios,
WACV20(1716-1725)
IEEE DOI 2006
Detectors, Object detection, Image resolution, Task analysis, Computer architecture, Pipelines, Training BibRef

Xu, S., Lan, S., Zhu, Q.,
MaskPlus: Improving Mask Generation for Instance Segmentation,
WACV20(2019-2027)
IEEE DOI 2006
Image segmentation, Semantics, Training, Proposals, Feature extraction, Task analysis, Object recognition BibRef

Shashidhara, B.M., Scott, M., Marburg, A.,
Instance Segmentation of Benthic Scale Worms at a Hydrothermal Site,
WACV20(1303-1312)
IEEE DOI 2006
Grippers, Vents, Image segmentation, Pipelines, Training, Training data, Cameras BibRef

Ahn, J.[Jiwoon], Cho, S.[Sunghyun], Kwak, S.[Suha],
Weakly Supervised Learning of Instance Segmentation With Inter-Pixel Relations,
CVPR19(2204-2213).
IEEE DOI 2002
BibRef

Ahn, J.[Jiwoon], Kwak, S.[Suha],
Learning Pixel-Level Semantic Affinity with Image-Level Supervision for Weakly Supervised Semantic Segmentation,
CVPR18(4981-4990)
IEEE DOI 1812
Image segmentation, Semantics, Training, Shape, Visualization, Pipelines, Motion segmentation BibRef

Wang, Y., Ramanan, D., Hebert, M.,
Meta-Learning to Detect Rare Objects,
ICCV19(9924-9933)
IEEE DOI 2004
convolutional neural nets, image classification, learning (artificial intelligence), object detection, Training BibRef

Hu, T., Mettes, P., Huang, J., Snoek, C.,
SILCO: Show a Few Images, Localize the Common Object,
ICCV19(5066-5075)
IEEE DOI 2004
convolutional neural nets, feature extraction, graph theory, image classification, learning (artificial intelligence), Machine learning BibRef

Deng, Z., Kong, Q., Murakami, T.,
Towards Efficient Instance Segmentation with Hierarchical Distillation,
TASKCV19(3243-3249)
IEEE DOI 2004
image segmentation, learning (artificial intelligence), object detection, distills pair-wise quantized feature maps, Knowledge distillation BibRef

Chen, X., Girshick, R., He, K., Dollar, P.,
TensorMask: A Foundation for Dense Object Segmentation,
ICCV19(2061-2069)
IEEE DOI 2004
convolutional neural nets, image segmentation, object detection, tensors, Mask R-CNN, dense sliding-window instance segmentation, Image segmentation BibRef

Fang, H., Sun, J., Wang, R., Gou, M., Li, Y., Lu, C.,
InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting,
ICCV19(682-691)
IEEE DOI 2004
Code, Segmentation.
WWW Link. convolutional neural nets, image annotation, image sampling, image segmentation, object detection, probability, Measurement BibRef

Ge, W., Huang, W., Guo, S., Scott, M.,
Label-PEnet: Sequential Label Propagation and Enhancement Networks for Weakly Supervised Instance Segmentation,
ICCV19(3344-3353)
IEEE DOI 2004
image classification, image representation, image segmentation, learning (artificial intelligence), object detection, Task analysis BibRef

Shaban, A., Rahimi, A., Bansal, S., Gould, S., Boots, B., Hartley, R.,
Learning to Find Common Objects Across Few Image Collections,
ICCV19(5116-5125)
IEEE DOI 2004
belief networks, greedy algorithms, inference mechanisms, learning (artificial intelligence), minimisation, Graphical models BibRef

Fu, C., Berg, T.L.[Tamara L.], Berg, A.C.[Alexander C.],
IMP: Instance Mask Projection for High Accuracy Semantic Segmentation of Things,
ICCV19(5177-5186)
IEEE DOI 2004
backpropagation, image segmentation, object detection, backpropagation, baseline semantic segmentation results, Object detection BibRef

Yang, L., Fan, Y., Xu, N.,
Video Instance Segmentation,
ICCV19(5187-5196)
IEEE DOI 2004
computer vision, convolutional neural nets, image segmentation, multimedia Web sites, object detection, object tracking, Object segmentation BibRef

Nassar, A.S.[Ahmed Samy], d'Aronco, S.[Stefano], Lefèvre, S.[Sébastien], Wegner, J.D.[Jan D.],
Geograph: Graph-based Multi-view Object Detection with Geometric Cues End-to-end,
ECCV20(VII:488-504).
Springer DOI 2011
BibRef
Earlier: A1, A3, A4, Only:
Simultaneous Multi-View Instance Detection With Learned Geometric Soft-Constraints,
ICCV19(6558-6567)
IEEE DOI 2004
geometry, learning (artificial intelligence), object detection, robust cross-view object detection, geometric soft constraints, Pose estimation BibRef

Sofiiuk, K., Sofiyuk, K., Barinova, O., Konushin, A., Barinova, O.,
AdaptIS: Adaptive Instance Selection Network,
ICCV19(7354-7362)
IEEE DOI 2004
Code, Segmentation.
WWW Link. image segmentation, object detection, AdaIN layers, pixel-accurate object masks, semantic segmentation pipeline, Aerospace electronics BibRef

Bolya, D.[Daniel], Foley, S.[Sean], Hays, J.[James], Hoffman, J.[Judy],
TIDE: A General Toolbox for Identifying Object Detection Errors,
ECCV20(III:558-573).
Springer DOI 2012
BibRef

Bolya, D., Zhou, C., Xiao, F., Lee, Y.J.,
YOLACT: Real-Time Instance Segmentation,
ICCV19(9156-9165)
IEEE DOI 2004
convolutional neural nets, image segmentation, learning (artificial intelligence), object detection, Task analysis BibRef

Lahoud, J., Ghanem, B., Oswald, M.R., Pollefeys, M.,
3D Instance Segmentation via Multi-Task Metric Learning,
ICCV19(9255-9265)
IEEE DOI 2004
image reconstruction, image representation, image segmentation, learning (artificial intelligence), stereo image processing, Measurement BibRef

Xu, W., Wang, H., Qi, F., Lu, C.,
Explicit Shape Encoding for Real-Time Instance Segmentation,
ICCV19(5167-5176)
IEEE DOI 2004
image segmentation, object detection, polynomials, tensors, object detection, explicit shape encoding, Training BibRef

Xiong, H., Lu, H., Liu, C., Liu, L., Cao, Z., Shen, C.,
From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer,
ICCV19(8361-8370)
IEEE DOI 2004
Code, Counting.
WWW Link. divide and conquer methods, image processing, learning (artificial intelligence), neural nets, Estimation BibRef

Shi, Z., Mettes, P.S.M.[Pascal S. M.], Snoek, C.G.M.[Cees G. M.],
Counting With Focus for Free,
ICCV19(4199-4208)
IEEE DOI 2004
Code, Counting.
WWW Link. convolutional neural nets, image segmentation, network theory (graphs), object detection, supervised learning, Convolution BibRef

Fan, R.C.[Ruo-Chen], Hou, Q.[Qibin], Cheng, M.M.[Ming-Ming], Yu, G.[Gang], Martin, R.R.[Ralph R.], Hu, S.M.[Shi-Min],
Associating Inter-image Salient Instances for Weakly Supervised Semantic Segmentation,
ECCV18(IX: 371-388).
Springer DOI 1810
BibRef

Luc, P.[Pauline], Couprie, C.[Camille], Le Cun, Y.[Yann], Verbeek, J.[Jakob],
Predicting Future Instance Segmentation by Forecasting Convolutional Features,
ECCV18(IX: 593-608).
Springer DOI 1810
BibRef

Luc, P.[Pauline], Neverova, N., Couprie, C.[Camille], Verbeek, J.[Jakob], Le Cun, Y.[Yann],
Predicting Deeper into the Future of Semantic Segmentation,
ICCV17(648-657)
IEEE DOI 1802
feedforward neural nets, image colour analysis, image resolution, image segmentation, image sequences, Training BibRef

Li, Y., Qi, H., Dai, J., Ji, X., Wei, Y.,
Fully Convolutional Instance-Aware Semantic Segmentation,
CVPR17(4438-4446)
IEEE DOI 1711
Convolution, Convolutional codes, Image segmentation, Object segmentation, Proposals, Semantics BibRef

Yao, J.[Jian], Boben, M.[Marko], Fidler, S.[Sanja], Urtasun, R.[Raquel],
Real-time coarse-to-fine topologically preserving segmentation,
CVPR15(2947-2955)
IEEE DOI 1510
BibRef

Gupta, A.[Agrim], Dollar, P.[Piotr], Girshick, R.[Ross],
LVIS: A Dataset for Large Vocabulary Instance Segmentation,
CVPR19(5351-5359).
IEEE DOI 2002
BibRef

Wang, X.L.[Xin-Long], Liu, S.[Shu], Shen, X.Y.[Xiao-Yong], Shen, C.H.[Chun-Hua], Jia, J.Y.[Jia-Ya],
Associatively Segmenting Instances and Semantics in Point Clouds,
CVPR19(4091-4100).
IEEE DOI 2002
BibRef

Hsu, K.J.[Kuang-Jui], Lin, Y.Y.[Yen-Yu], Chuang, Y.Y.[Yung-Yu],
DeepCO3: Deep Instance Co-Segmentation by Co-Peak Search and Co-Saliency Detection,
CVPR19(8838-8847).
IEEE DOI 2002
BibRef

Araslanov, N.[Nikita], Rothkopf, C.A.[Constantin A.], Roth, S.[Stefan],
Actor-Critic Instance Segmentation,
CVPR19(8229-8238).
IEEE DOI 2002
BibRef

Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.,
Path Aggregation Network for Instance Segmentation,
CVPR18(8759-8768)
IEEE DOI 1812
Proposals, Feature extraction, Task analysis, Image segmentation, Object detection, Training, Semantics BibRef

Chen, L., Hermans, A., Papandreou, G., Schroff, F., Wang, P., Adam, H.,
MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features,
CVPR18(4013-4022)
IEEE DOI 1812
Semantics, Agriculture, Image segmentation, Object detection, Convolution, Feature extraction, Encoding BibRef

Neven, D.[Davy], de Brabandere, B.[Bert], Proesmans, M.[Marc], Van Gool, L.J.[Luc J.],
Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth,
CVPR19(8829-8837).
IEEE DOI 2002
BibRef

Qi, L.[Lu], Jiang, L.[Li], Liu, S.[Shu], Shen, X.Y.[Xiao-Yong], Jia, J.Y.[Jia-Ya],
Amodal Instance Segmentation With KINS Dataset,
CVPR19(3009-3018).
IEEE DOI 2002
BibRef

Pham, Q.H.[Quang-Hieu], Nguyen, T.[Thanh], Hua, B.S.[Binh-Son], Roig, G.[Gemma], Yeung, S.K.[Sai-Kit],
JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds With Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields,
CVPR19(8819-8828).
IEEE DOI 2002
BibRef

Chen, K.[Kai], Pang, J.M.[Jiang-Miao], Wang, J.Q.[Jia-Qi], Xiong, Y.[Yu], Li, X.X.[Xiao-Xiao], Sun, S.Y.[Shu-Yang], Feng, W.[Wansen], Liu, Z.[Ziwei], Shi, J.P.[Jian-Ping], Ouyang, W.L.[Wan-Li], Loy, C.C.[Chen Change], Lin, D.[Dahua],
Hybrid Task Cascade for Instance Segmentation,
CVPR19(4969-4978).
IEEE DOI 2002
BibRef

Elich, C.[Cathrin], Engelmann, F.[Francis], Kontogianni, T.[Theodora], Leibe, B.[Bastian],
3D Bird's-Eye-View Instance Segmentation,
GCPR19(48-61).
Springer DOI 1911
BibRef

Shang, C., Wu, Q., Meng, F., Xu, L.,
Instance Segmentation by Learning Deep Feature in Embedding Space,
ICIP19(2444-2448)
IEEE DOI 1910
Instance Segmentation, Instance Discrimination Network, Embedding Space, Deep Feature BibRef

Liu, Y.F.[Yan-Feng], Psota, E.T.[Eric T.], Pérez, L.C.[Lance C.],
Layered Embeddings for Amodal Instance Segmentation,
ICIAR19(I:102-111).
Springer DOI 1909
Code, Segmentation. Code available:
WWW Link. BibRef

Couprie, C.[Camille], Luc, P.[Pauline], Verbeek, J.[Jakob],
Joint Future Semantic and Instance Segmentation Prediction,
AnticipateBeh18(III:154-168).
Springer DOI 1905
BibRef

Halupka, K., Garnavi, R., Moore, S.,
Deep Semantic Instance Segmentation of Tree-Like Structures Using Synthetic Data,
WACV19(1713-1722)
IEEE DOI 1904
data analysis, feature extraction, image segmentation, learning (artificial intelligence), neural nets, Periodic structures BibRef

Follmann, P.[Patrick], Nig, R.K.[Rebecca Kö], Rtinger, P.H.[Philipp Hä], Klostermann, M.[Michael], Ttger, T.B.[Tobias Bö],
Learning to See the Invisible: End-to-End Trainable Amodal Instance Segmentation,
WACV19(1328-1336)
IEEE DOI 1904
image segmentation, learning (artificial intelligence), object detection, COCOA cls, D2S amodal, COCO amodal dataset, BibRef

Li, K.[Ke], Malik, J.[Jitendra],
Amodal Instance Segmentation,
ECCV16(II: 677-693).
Springer DOI 1611
predict the region encompassing both visible and occluded parts of each object. BibRef

Li, K.[Ke], Hariharan, B.[Bharath], Malik, J.[Jitendra],
Iterative Instance Segmentation,
CVPR16(3659-3667)
IEEE DOI 1612
BibRef

Li, Z.X.[Zuo-Xin], Zhou, F.Q.[Fu-Qiang], Yang, L.[Lu],
Fast Single Shot Instance Segmentation,
ACCV18(IV:257-272).
Springer DOI 1906
BibRef

Manohar, K.V., Niitani, Y.[Yusuke],
An End-to-End Tree Based Approach for Instance Segmentation,
POCV18(V:521-527).
Springer DOI 1905
BibRef

Liu, Y., Wang, R., Shan, S., Chen, X.,
Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships,
CVPR18(6985-6994)
IEEE DOI 1812
Object detection, Feature extraction, Logic gates, Visualization, Detectors, Context modeling, Image edge detection BibRef

Zhou, Y.Z.[Yan-Zhao], Zhu, Y.[Yi], Ye, Q.X.[Qi-Xiang], Qiu, Q.[Qiang], Jiao, J.B.[Jian-Bin],
Weakly Supervised Instance Segmentation Using Class Peak Response,
CVPR18(3791-3800)
IEEE DOI 1812
Image segmentation, Visualization, Training, Semantics, Proposals, Image color analysis, Kernel BibRef

Liu, Y.D.[Yi-Ding], Yang, S.[Siyu], Li, B.[Bin], Zhou, W.G.[Wen-Gang], Xu, J.Z.[Ji-Zheng], Li, H.Q.A.[Hou-Qi-Ang], Lu, Y.[Yan],
Affinity Derivation and Graph Merge for Instance Segmentation,
ECCV18(III: 708-724).
Springer DOI 1810
BibRef

Novotny, D.[David], Albanie, S.[Samuel], Larlus, D.[Diane], Vedaldi, A.[Andrea],
Semi-convolutional Operators for Instance Segmentation,
ECCV18(I: 89-105).
Springer DOI 1810
BibRef

Margffoy-Tuay, E.[Edgar], Pérez, J.C.[Juan C.], Botero, E.[Emilio], Arbeláez, P.[Pablo],
Dynamic Multimodal Instance Segmentation Guided by Natural Language Queries,
ECCV18(XI: 656-672).
Springer DOI 1810
BibRef

Xu, W.Q.[Wen-Qiang], Li, Y.L.[Yong-Lu], Lu, C.W.[Ce-Wu],
SRDA: Generating Instance Segmentation Annotation via Scanning, Reasoning and Domain Adaptation,
ECCV18(XII: 124-140).
Springer DOI 1810
BibRef

Pham, T.[Trung], Kumar, B.G.V.[B. G. Vijay], Do, T.T.[Thanh-Toan], Carneiro, G.[Gustavo], Reid, I.D.[Ian D.],
Bayesian Semantic Instance Segmentation in Open Set World,
ECCV18(X: 3-18).
Springer DOI 1810
BibRef

Ren, M.Y.[Meng-Ye], Zemel, R.S.[Richard S.],
End-to-End Instance Segmentation with Recurrent Attention,
CVPR17(293-301)
IEEE DOI 1711
Computational modeling, Convolution, Image segmentation, Indexes, Training. Counting. BibRef

Chattopadhyay, P., Vedantam, R., Selvaraju, R.R., Batra, D., Parikh, D.,
Counting Everyday Objects in Everyday Scenes,
CVPR17(4428-4437)
IEEE DOI 1711
Detectors, Feature extraction, Knowledge discovery, Object detection, Surveillance, Visualization BibRef

Li, Y.[Yao], Liu, L.Q.[Ling-Qiao], Shen, C.H.[Chun-Hua], van den Hengel, A.[Anton],
Image Co-Localization by Mimicking a Good Detector's Confidence Score Distribution,
ECCV16(II: 19-34).
Springer DOI 1611
identify each instance in multiple images. BibRef

Chen, Y.T.[Yi-Ting], Liu, X.[Xiaokai], Yang, M.H.[Ming-Hsuan],
Multi-instance object segmentation with occlusion handling,
CVPR15(3470-3478)
IEEE DOI 1510
BibRef

Liu, B.Y.[Bu-Yu], He, X.M.[Xu-Ming],
Multiclass semantic video segmentation with object-level active inference,
CVPR15(4286-4294)
IEEE DOI 1510
BibRef

Liu, B.Y.[Bu-Yu], He, X.M.[Xu-Ming], Gould, S.[Stephen],
Multi-class Semantic Video Segmentation with Exemplar-Based Object Reasoning,
WACV15(1014-1021)
IEEE DOI 1503
Cognition BibRef

Chang, F.J.[Feng-Ju], Lin, Y.Y.[Yen-Yu], Hsu, K.J.[Kuang-Jui],
Multiple Structured-Instance Learning for Semantic Segmentation with Uncertain Training Data,
CVPR14(360-367)
IEEE DOI 1409
BibRef

He, X.M.[Xu-Ming], Gould, S.[Stephen],
An Exemplar-Based CRF for Multi-instance Object Segmentation,
CVPR14(296-303)
IEEE DOI 1409
BibRef
Earlier:
Multi-instance Object Segmentation with Exemplars,
GMSU13(1-4)
IEEE DOI 1403
Markov processes BibRef

Vezhnevets, A.[Alexander], Ferrari, V.[Vittorio],
Associative Embeddings for Large-Scale Knowledge Transfer with Self-Assessment,
CVPR14(1987-1994)
IEEE DOI 1409
ImageNet BibRef

Vezhnevets, A.[Alexander], Buhmann, J.M.[Joachim M.], Ferrari, V.[Vittorio],
Active learning for semantic segmentation with expected change,
CVPR12(3162-3169).
IEEE DOI 1208
BibRef

Vezhnevets, A.[Alexander], Ferrari, V.[Vittorio], Buhmann, J.M.[Joachim M.],
Weakly supervised structured output learning for semantic segmentation,
CVPR12(845-852).
IEEE DOI 1208
BibRef
Earlier:
Weakly supervised semantic segmentation with a multi-image model,
ICCV11(643-650).
IEEE DOI 1201
BibRef

Vezhnevets, A.[Alexander], Buhmann, J.M.[Joachim M.],
Towards weakly supervised semantic segmentation by means of multiple instance and multitask learning,
CVPR10(3249-3256).
IEEE DOI 1006

See also Agnostic Domain Adaptation. BibRef

Fiaschi, L.[Luca], Koethe, U.[Ullrich], Nair, R.[Rahul], Hamprecht, F.A.[Fred A.],
Learning to count with regression forest and structured labels,
ICPR12(2685-2688).
WWW Link. 1302
count instances BibRef

Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Panoptic Segmentation .


Last update:Oct 20, 2021 at 09:45:26