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
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
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 .