Summary of Motif Guided Graph Transformer with Combinatorial Skeleton Prototype Learning For Skeleton-based Person Re-identification, by Haocong Rao et al.
Motif Guided Graph Transformer with Combinatorial Skeleton Prototype Learning for Skeleton-Based Person Re-Identification
by Haocong Rao, Chunyan Miao
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to person re-identification (re-ID) using 3D skeleton data. The proposed method, MoCos, leverages the structure-specific and gait-related body relations as well as combinatorial features of skeleton graphs to learn effective skeleton representations for person re-ID. The MoCos model consists of two main components: the motif guided graph transformer (MGT) and the combinatorial skeleton prototype learning (CSP). MGT incorporates hierarchical structural motifs and gait collaborative motifs, which simultaneously focus on multi-order local joint correlations and key cooperative body parts to enhance skeleton relation learning. CSP leverages random spatial-temporal combinations of joint nodes and skeleton graphs to generate diverse sub-skeleton and sub-tracklet representations, which are contrasted with the most representative features (prototypes) of each identity to learn class-related semantics and discriminative skeleton representations. The paper demonstrates the superior performance of MoCos over existing state-of-the-art models in person re-ID tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to recognize people using 3D data from their skeletons. This method, called MoCos, uses information about how our bodies move and work together to better identify people. It’s like using clues from how we walk or run to figure out who someone is. The researchers developed two main parts of this approach: MGT and CSP. MGT looks at the connections between different body parts and how they work together. CSP generates many different patterns from these connections, which helps the model learn what makes each person unique. The paper shows that MoCos does a better job than other methods at recognizing people. |
Keywords
» Artificial intelligence » Semantics » Transformer
