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RMPE: Regional Multi-person Pose Estimation

This paper proposes a novel regional multi-person pose estimation (RMPE) framework to facilitate pose estimation in the presence of inaccurate human bounding boxes and can achieve 76:7 mAP on the MPII (multi person) dataset.
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CrowdPose: Efficient Crowded Scenes Pose Estimation and a New Benchmark

A novel and efficient method to tackle the problem of pose estimation in the crowd and a new dataset to better evaluate algorithms and results on MSCOCO dataset demonstrate the generalization ability of the proposed method.
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Transferable Interactiveness Knowledge for Human-Object Interaction Detection

The core idea is to exploit an Interactiveness Network to learn the general interactiveness knowledge from multiple HOI datasets and perform Non-Interaction Suppression before HOI classification in inference and extensively evaluate the proposed method on HICO-DET and V-COCO datasets.
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GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping

This work contributes a large-scale grasp pose detection dataset with a unified evaluation system and proposes an end-to-end grasp pose prediction network given point cloud inputs, where the network learns approaching direction and operation parameters in a decoupled manner.
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Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation

This paper proposes a pose grammar to tackle the problem of 3D human pose estimation, which takes 2D pose as input and learns a generalized 2D-3D mapping function and enforces high-level constraints over human poses.
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InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting

This paper presents a simple, efficient and effective method to augment the training set using the existing instance mask annotations, and proposes a location probability map based approach to explore the feasible locations that objects can be placed based on local appearance similarity.
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Cross-Domain Adaptation for Animal Pose Estimation

This paper proposed a novel cross-domain adaptation method to transform the animal pose knowledge from labeled animal classes to unlabeled animal classes, and uses the modest animal pose dataset to adapt learned knowledge to multiple animals species.
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PaStaNet: Toward Human Activity Knowledge Engine

This work proposes a new path: infer human part states first and then reason out the activities based on part-level semantics, which achieves significant improvements, e.g. 6.4 and 13.9 mAP on full and one-shot sets of HICO in supervised learning, and 3.2 and 4.2 mAP in transfer learning.
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Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer

A novel method to generate synthetic human part segmentation data using easily-obtained human keypoint annotations to exploit the anatomical similarity among human to transfer the parsing results of a person to another person with similar pose.
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Transferable Interactiveness Prior for Human-Object Interaction Detection

The core idea is to exploit an Interactiveness Network to learn the general interactiveness prior from multiple HOI datasets and perform Non-Interaction Suppression before HOI classification in inference and extensively evaluate the proposed method on HICO-DET and V-COCO datasets.
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