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

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


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

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

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

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., Berg, A.,
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., Lefèvre, S., Wegner, J.D.,
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., 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

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

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

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:Sep 14, 2020 at 15:32:18