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Instance segmentation实例分割（Instance Segmentation）是视觉经典四个任务中相对最难的一个，它既具备语义分割（Semantic Segmentation）的特点，需要做到像素层面上的分类，也具备目标检测（Object Detection）的一部分特点，即需要定位出不同实例，即使它们是同一种类。因此，实例分割的研究长期以来都有着两条线，分别是自下而上的基于语义分割的方法和自上而下的基于检测的方法，这两种方法都属于两阶段的方法。
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BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation
Chen Hao, Sun Kunyang,Tian Zhi,Shen Chunhua, Huang Yongming,Yan Youliang
CVPR, pp.8570-8578, (2020)
Methods
Conclusion
As shown in our experiments in Section 4, our blender module improves the performance of bases combination methods comparing to YOLACT and FCIS by a large margin without increasing computation complexity
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Cited by12BibtexViews50Links
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Learning Instance Occlusion for Panoptic Segmentation
Justin Lazarow,Kwonjoon Lee, Kunyu Shi,Zhuowen Tu
CVPR, pp.10717-10726, (2020)
Conclusion
We have introduced an explicit notion of instance occlusion to Mask R-CNN so that instances may be effectively fused when producing a panoptic segmentation
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Cited by6BibtexViews27Links
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D2Det: Towards High Quality Object Detection and Instance Segmentation
Jiale Cao,Hisham Cholakkal,Rao Muhammad Anwer,Fahad Shahbaz Khan,Yanwei Pang,Ling Shao
CVPR, pp.11482-11491, (2020)
Methods
Conclusion
We introduce dense local regression that predicts multiple dense box offsets for a proposal
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Cited by5BibtexViews117Links
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Mask Encoding for Single Shot Instance Segmentation
Zhang Rufeng,Tian Zhi,Shen Chunhua,You Mingyu,Yan Youliang
CVPR, pp.10223-10232, (2020)
Methods
Conclusion
We have introduced a new, simple singleshot instance segmentation framework termed mask encoding based instance segmentation
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Cited by3BibtexViews60Links
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Deep Snake for Real-Time Instance Segmentation
Sida Peng, Wen Jiang,Huaijin Pi,Xiuli Li,Hujun Bao,Xiaowei Zhou
CVPR, pp.8530-8539, (2020)
Methods
Conclusion
We proposed a learning-based snake algorithm for realtime instance segmentation, which introduces the circular convolution for efficient feature learning on the contour and regresses vertex-wise offsets for the contour deformation
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Cited by1BibtexViews65Links
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LevelSet R-CNN: A Deep Variational Method for Instance Segmentation
Namdar Homayounfar, Yuwen Xiong, Justin Liang,Wei-Chiu Ma,Raquel Urtasun
european conference on computer vision, pp.555-571, (2020)
Methods
Conclusion
We proposed LevelSet R-CNN which combines the strengths of modern deep learning based Mask R-CNN and classical energy based ChanVese level set segmentation framework in an end-to-end manner
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Cited by0BibtexViews39Links
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Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation
Yu Xiang,Christopher Xie, Arsalan Mousavian,Dieter Fox
Methods
Conclusion
We have introduced a simple but effective approach for Unseen Object Instance Segmentation by learning RGB-D feature embeddings from synthetic data
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Cited by0BibtexViews18Links
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Commonality-Parsing Network across Shape and Appearance for Partially Supervised Instance Segmentation
Qi Fan,Lei Ke,Wenjie Pei,Chi-Keung Tang,Yu-Wing Tai
european conference on computer vision, pp.379-396, (2020)
Methods
Conclusion
In this paper we present a novel “Commonality-Parsing Network” for partially supervised instance segmentation
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Cited by0BibtexViews33Links
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Deep Variational Instance Segmentation
Jialin Yuan,Chao Chen,Li Fuxin
NIPS 2020, (2020)
Methods
Conclusion
In this paper we proposed deep variational instance segmentation, which relaxes instance segmentation into a variational problem with a novel variational objective that includes a permutationinvariant component
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Cited by0BibtexViews11Links
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Weakly Supervised Instance Segmentation by Learning Annotation Consistent Instances
Aditya Arun,C. V. Jawahar,M. Pawan Kumar
european conference on computer vision, pp.254-270, (2020)
Methods
Conclusion
Following the same evaluation protocol from other competing approaches, we report mean average precision with four intersection over union thresholds, denoted by mAPkr where k denotes the different values of IoU and k = {0.25, 0.50, 0.70, 0.75}
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Cited by0BibtexViews68Links
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Unseen Object Instance Segmentation for Robotic Environments
Christopher Xie, Yu Xiang, Arsalan Mousavian,Dieter Fox
In order to function in unstructured environments, robots need the ability to recognize unseen objects. We take a step in this direction by tackling the problem of segmenting unseen object instances in tabletop environments. However, the type of large-scale real-world dataset r...
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Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation
Fangyun Wei,Xiao Sun,Hongyang Li,Jingdong Wang,Stephen Lin
european conference on computer vision, pp.527-544, (2020)
Methods
Conclusion
We propose Point-Set Anchors which can be seen as a generalization and extension of classical anchors for high-level recognition tasks such as instance segmentation and pose estimation
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Cited by0BibtexViews43Links
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A Survey on Instance Segmentation: State of the art
Abdul Mueed Hafiz, Ghulam Mohiuddin Bhat
International Journal of Multimedia Information Retrieval, no. 3 (2020): 171-189
Methods
Conclusion
The popular datasets used for instance segmentation has been discussed
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Cited by0BibtexViews5Links
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PatchPerPix for Instance Segmentation
Hirsch Peter, Mais Lisa,Kainmueller Dagmar
Conclusion
In this work we present a novel generic method for instance segmentation that comprises a CNN to predict dense local shape descriptors and a one-pass instance assembly pipeline
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Cited by0BibtexViews8Links
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Hybrid Task Cascade for Instance Segmentation
Kai Chen,Jiangmiao Pang,Jiaqi Wang,Yu Xiong,Xiaoxiao Li,Shuyang Sun, Wansen Feng,Ziwei Liu,Jianping Shi,Wanli Ouyang,Chen Change Loy,Dahua Lin
CVPR, (2019): 4974-4983
Methods
Conclusion
We propose Hybrid Task Cascade, a new cascade architecture for instance segmentation
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Cited by125BibtexViews117Links
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3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans
Ji Hou,Angela Dai,Matthias Nießner
CVPR, (2019)
Methods
Conclusion
We introduce 3D-SIS, a new approach for 3D semantic instance segmentation of RGB-D scans, which is trained in an end-to-end fashion to detect object instances and infer a per-voxel 3D semantic instance segmentation
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Cited by58BibtexViews52Links
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LVIS - A Dataset for Large Vocabulary Instance Segmentation
Agrim Gupta,Piotr Dollár,Ross B. Girshick
CVPR, pp.5356-5364, (2019)
Conclusion
We introduce LVIS: a new dataset for Large Vocabulary Instance Segmentation
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Cited by51BibtexViews206Links
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YOLACT: Real-time Instance Segmentation
Daniel Bolya, Chong Zhou,Fanyi Xiao,Yong Jae Lee
arXiv: Computer Vision and Pattern Recognition, (2019): 9157-9166
Methods
Conclusion
Our goal is to fill that gap with a fast, one-stage instance segmentation model in the same way that SSD and YOLO fill that gap for object detection
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Cited by51BibtexViews26Links
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GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud
Li Yi,Wang Zhao,He Wang,Minhyuk Sung,Leonidas J. Guibas
CVPR, (2019): 3947-3956
Methods
Conclusion
We demonstrate how Generative Shape Proposal Network could be incorporated into a novel 3D instance segmentation framework: R-PointNet, and achieve state-of-the-art performance on several instance segmentation benchmarks
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Cited by47BibtexViews67Links
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JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields
Quang-Hieu Pham,Duc Thanh Nguyen,Binh-Son Hua,Gemma Roig,Sai-Kit Yeung
CVPR, (2019): 8827-8836
Methods
Conclusion
This paper proposes a semantic-instance segmentation method that jointly performs both of the tasks via a novel multi-task pointwise network and a multi-value conditional random field model
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Cited by33BibtexViews50Links
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Keywords
Deep LearningBackbone NetworkColorectal AdenocarcinomaComputational PathologyConvolutional Neural NetworksGland Instance SegmentationInstance SegmentationMachine VisionMultiple Instance LearningObject Detection
Authors
Raquel Urtasun
Raquel Urtasun
Paper 5
Shu Liu
Shu Liu
Paper 4
Chunhua Shen
Chunhua Shen
Paper 4
Fahad Shahbaz Khan
Fahad Shahbaz Khan
Paper 3
Cewu Lu
Cewu Lu
Paper 3
Jitendra Malik
Jitendra Malik
Paper 3
Dieter Fox
Dieter Fox
Paper 3
Philip Torr
Philip Torr
Paper 3
Jiaya Jia
Jiaya Jia
Paper 3
Christopher Xie
Christopher Xie
Paper 3
IJCAI 2020cvpr2020Papers, Authors, Comments