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。
Chen Hao, Sun Kunyang,Tian Zhi,Shen Chunhua, Huang Yongming,Yan Youliang
CVPR, pp.8570-8578, (2020)
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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CVPR, pp.10717-10726, (2020)
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
Cited by6BibtexViews27Links
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CVPR, pp.11482-11491, (2020)
We introduce dense local regression that predicts multiple dense box offsets for a proposal
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CVPR, pp.10223-10232, (2020)
We have introduced a new, simple singleshot instance segmentation framework termed mask encoding based instance segmentation
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CVPR, pp.8530-8539, (2020)
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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Namdar Homayounfar, Yuwen Xiong, Justin Liang,Wei-Chiu Ma,Raquel Urtasun
european conference on computer vision, pp.555-571, (2020)
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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Yu Xiang,Christopher Xie, Arsalan Mousavian,Dieter Fox
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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european conference on computer vision, pp.379-396, (2020)
In this paper we present a novel “Commonality-Parsing Network” for partially supervised instance segmentation
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NIPS 2020, (2020)
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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european conference on computer vision, pp.254-270, (2020)
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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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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european conference on computer vision, pp.527-544, (2020)
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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Abdul Mueed Hafiz, Ghulam Mohiuddin Bhat
International Journal of Multimedia Information Retrieval, no. 3 (2020): 171-189
The popular datasets used for instance segmentation has been discussed
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Hirsch Peter, Mais Lisa,Kainmueller Dagmar
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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CVPR, (2019): 4974-4983
We propose Hybrid Task Cascade, a new cascade architecture for instance segmentation
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CVPR, (2019)
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
Cited by58BibtexViews52Links
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CVPR, pp.5356-5364, (2019)
We introduce LVIS: a new dataset for Large Vocabulary Instance Segmentation
Cited by51BibtexViews206Links
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Daniel Bolya, Chong Zhou,Fanyi Xiao,Yong Jae Lee
arXiv: Computer Vision and Pattern Recognition, (2019): 9157-9166
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
Cited by51BibtexViews26Links
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CVPR, (2019): 3947-3956
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
Cited by47BibtexViews67Links
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CVPR, (2019): 8827-8836
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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Keywords
Deep LearningBackbone NetworkColorectal AdenocarcinomaComputational PathologyConvolutional Neural NetworksGland Instance SegmentationInstance SegmentationMachine VisionMultiple Instance LearningObject Detection
Authors
Raquel Urtasun
Paper 5
Shu Liu
Paper 4
Chunhua Shen
Paper 4
Fahad Shahbaz Khan
Paper 3
Cewu Lu
Paper 3
Jitendra Malik
Paper 3
Dieter Fox
Paper 3
Philip Torr
Paper 3
Jiaya Jia
Paper 3
Christopher Xie
Paper 3