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Associative Embedding:End-to-End Learning for Joint Detection and Grouping
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TLDR
Associative embedding is introduced, a novel method for supervising convolutional neural networks for the task of detection and grouping for multi-person pose estimation and state-of-the-art performance on the MPII and MS-COCO datasets is reported.Abstract:
We introduce associative embedding, a novel method for supervising convolutional neural networks for the task of detection and grouping. A number of computer vision problems can be framed in this manner including multi-person pose estimation, instance segmentation, and multi-object tracking. Usually the grouping of detections is achieved with multi-stage pipelines, instead we propose an approach that teaches a network to simultaneously output detections and group assignments. This technique can be easily integrated into any state-of-the-art network architecture that produces pixel-wise predictions. We show how to apply this method to both multi-person pose estimation and instance segmentation and report state-of-the-art performance for multi-person pose on the MPII and MS-COCO datasets.read more
Citations
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Proceedings ArticleDOI
Cascaded Pyramid Network for Multi-person Pose Estimation
TL;DR: A novel network structure called Cascaded Pyramid Network (CPN) is presented which targets to relieve the problem from these "hard" keypoints, with state-of-art results on the COCO keypoint benchmark, with average precision at 73.0.
Proceedings ArticleDOI
SGN: Sequential Grouping Networks for Instance Segmentation
TL;DR: This paper proposes Sequential Grouping Networks, a sequence of neural networks, each solving a sub-grouping problem of increasing semantic complexity in order to gradually compose objects out of pixels to tackle the problem of object instance segmentation.
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Recurrent Pixel Embedding for Instance Grouping
Shu Kong,Charless C. Fowlkes +1 more
TL;DR: In this paper, a differentiable, end-to-end trainable framework for solving pixel-level grouping problems such as instance segmentation consisting of two novel components is introduced. But the choice of embedding dimension and margin, relating them to theoretical results on the problem of distributing points uniformly on the sphere, is discussed.
Journal ArticleDOI
Multi-animal pose estimation, identification and tracking with DeepLabCut
Jessy Lauer,Mu Zhou,Shaokai Ye,William Menegas,Steffen Schneider,Tanmay Nath,Mohammed Mostafizur Rahman,Valentina Di Santo,Daniel Soberanes,Guoping Feng,Venkatesh N. Murthy,George Lauder,Catherine Dulac,Mackenzie W. Mathis,Alexander Mathis +14 more
TL;DR: In this article , a pose estimation toolbox for multi-animal tracking is presented, which integrates the ability to predict an animal's identity to assist tracking in case of occlusions.
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Multiple-Human Parsing in the Wild
Jianshu Li,Jian Zhao,Yunchao Wei,Congyan Lang,Yidong Li,Terence Sim,Shuicheng Yan,Jiashi Feng +7 more
TL;DR: This work introduces a new multi-human parsing dataset and a novel multi- human parsing model named MH-Parser, which generates global parsing maps and person instance masks simultaneously in a bottom-up fashion with the help of a new Graph-GAN model.
References
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