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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Citations
Astroglial networks control visual responses of superior collicular neurons and sensory-motor behavior
Josien Visser,Giampaolo Milior,Rachel Breton,Julien Moulard,M.A. Garnero,Giselle Cheung,Jérôme Ribot,Nathalie Rouach +7 more
TL;DR: It is reported that astrocytes from the mouse SC form extensive networks in the retinorecipient layer compared to visual cortex, indicating that astroglial networks shape synaptic circuit activity underlying SC functional visual responses and play a crucial role in integrating visual cues to drive sensory-motor behavior.
1
Deep Transfer Feature Based Convolutional Neural Forests for Head Pose Estimation
Yuanyuan Liu,Zhong Xie,Xi Gong,Fang Fang +3 more
- 20 Nov 2017
TL;DR: A novel deep transfer feature based on convolutional neural forest method (D-CNF) is proposed for head pose estimation that achieves much improved performance and great robustness and performs well even when there is only a small amount of training data.
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Lend Me a Hand: Auxiliary Image Data Helps Interaction Detection
Coert van Gemeren,Ronald Poppe,Remco C. Veltkamp +2 more
- 01 May 2017
TL;DR: This paper introduces a method to train body part detectors from nonspecific images with pose information and introduces a training scheme and an adapted DPM formulation to allow for the inclusion of this auxiliary data.
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Human Pose Estimation via Dynamic Information Transfer
TL;DR: Li et al. as mentioned in this paper proposed a dynamic information transfer network (DITN) to improve the pose estimation with the spatial relationship of adjacent joints, which achieved 90.8% PCKh@0.5 on MPII and 75.0% AP on COCO.
Deep Mixture of MRFs for Human Pose Estimation
Ioannis Marras,Petar Palasek,Ioannis Patras +2 more
- 02 Dec 2018
TL;DR: This paper proposes to replace the auto-encoder network layer with a layer that implements a Gaussian mixture model (GMM) that provides a soft clustering of the human pose predictions in an online fashion that is the first time that a clustering algorithm like GMM is used in anOnline fashion for the problem of 2D human pose estimation.
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