Open AccessPosted Content
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
Eldar Insafutdinov,Leonid Pishchulin,Bjoern Andres,Mykhaylo Andriluka,Mykhaylo Andriluka,Bernt Schiele +5 more
TL;DR: In this paper, an incremental optimization strategy was proposed to explore the search space more efficiently, leading both to better performance and significant speed-up factors for multi-person pose estimation.
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Abstract: The goal of this paper is to advance the state-of-the-art of articulated pose estimation in scenes with multiple people. To that end we contribute on three fronts. We propose (1) improved body part detectors that generate effective bottom-up proposals for body parts; (2) novel image-conditioned pairwise terms that allow to assemble the proposals into a variable number of consistent body part configurations; and (3) an incremental optimization strategy that explores the search space more efficiently thus leading both to better performance and significant speed-up factors. Evaluation is done on two single-person and two multi-person pose estimation benchmarks. The proposed approach significantly outperforms best known multi-person pose estimation results while demonstrating competitive performance on the task of single person pose estimation. Models and code available at this http URL
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Bottom-up Higher-Resolution Networks for Multi-Person Pose Estimation
Bowen Cheng,Bin Xiao,Jingdong Wang,Honghui Shi,Thomas S. Huang,Lei Zhang +5 more
- 27 Aug 2019
TL;DR: Higher-Resolution Network (HigherHRNet) is proposed, which is a simple extension of the High-Res resolution Network (HRNet), which generates higher-resolution feature maps by deconvolving the high- resolution feature maps outputted by HRNet, which are spatially more accurate for small and medium persons.
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•Posted Content
Cascade Feature Aggregation for Human Pose Estimation
TL;DR: A novel Cascade Feature Aggregation (CFA) method, which cascades several hourglass networks for robust human pose estimation, which outperforms the state-of-the-art and achieves the best performance on the state of theart benchmark MPII.
anTraX, a software package for high-throughput video tracking of color-tagged insects
TL;DR: An algorithm and software package for high-throughput video tracking of color-tagged insects that combines neural network classification of animals with a novel approach for representing tracking data as a graph, enabling individual tracking even in cases where it is difficult to segment animals from one another, or where tags are obscured.
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Anchor Loss: Modulating Loss Scale Based on Prediction Difficulty
Serim Ryou,Seong-Gyun Jeong,Pietro Perona +2 more
- 01 Oct 2019
TL;DR: In this paper, a novel loss function is proposed to dynamically re-scales the cross entropy based on prediction difficulty regarding a sample, where the prediction difficulty is defined as a relative property coming from the confidence score gap between positive and negative labels.
A dual-source approach for 3D human pose estimation from single images
TL;DR: In this paper, a dual-source approach is proposed to estimate 2D pose from motion capture data and then estimate the 3D pose map from the 2D motion capture space to the image.
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