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Found 8 papers, 5 papers with code
X-HRNet: Towards Lightweight Human Pose Estimation with Spatially Unidimensional Self-Attention
Inspired by this observation, we introduce a lightweight and powerful alternative, Spatially Unidimensional Self-Attention (SUSA), to the pointwise (1x1) convolution that is the main computational bottleneck in the depthwise separable 3c3 convolution.
CIParsing: Unifying Causality Properties into Multiple Human Parsing
Existing methods of multiple human parsing (MHP) apply statistical models to acquire underlying associations between images and labeled body parts.
RepParser: End-to-End Multiple Human Parsing with Representative Parts
Different from mainstream methods, RepParser solves the multiple human parsing in a new single-stage manner without resorting to person detection or post-grouping. To this end, RepParser decouples the parsing pipeline into instance-aware kernel generation and part-aware human parsing, which are responsible for instance separation and instance-specific part segmentation, respectively.
Skeleton-based Action Recognition via Adaptive Cross-Form Learning
Specifically, each GCN model in ACFL not only learns action representation from the single-form skeletons, but also adaptively mimics useful representations derived from other forms of skeletons.
KE-RCNN: Unifying Knowledge based Reasoning into Part-level Attribute Parsing
In this article, we propose a Knowledge Embedded RCNN (KE-RCNN) to identify attributes by leveraging rich knowledges, including implicit knowledge (e. g., the attribute ``above-the-hip'' for a shirt requires visual/geometry relations of shirt-hip) and explicit knowledge (e. g., the part of ``shorts'' cannot have the attribute of ``hoodie'' or ``lining'').
KTN: Knowledge Transfer Network for Learning Multi-person 2D-3D Correspondences
Human densepose estimation, aiming at establishing dense correspondences between 2D pixels of human body and 3D human body template, is a key technique in enabling machines to have an understanding of people in images.
Technical Report: Disentangled Action Parsing Networks for Accurate Part-level Action Parsing
Despite of dramatic progresses in the area of video classification research, a severe problem faced by the community is that the detailed understanding of human actions is ignored.
From General to Specific: Informative Scene Graph Generation via Balance Adjustment
The scene graph generation (SGG) task aims to detect visual relationship triplets, i. e., subject, predicate, object, in an image, providing a structural vision layout for scene understanding.
