Summary of Relation Learning and Aggregate-attention For Multi-person Motion Prediction, by Kehua Qu et al.
Relation Learning and Aggregate-attention for Multi-person Motion Prediction
by Kehua Qu, Rui Ding, Jin Tang
First submitted to arxiv on: 6 Nov 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 proposed framework for multi-person motion prediction addresses the limitations of previous methods by explicitly modeling intra-relations within individuals and inter-relations among groups. The framework consists of a GCN-based network for intra-relations and a novel reasoning network for inter-relations, which are integrated using an Interaction Aggregation Module (IAM) that employs an aggregate-attention mechanism. This module can also be applied to other dual-path models. Experimental results on various datasets demonstrate the state-of-the-art performance of the proposed method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to predict where a group of people will move next, considering how each person’s joints relate to their own body and how they interact with others. Previous methods did a good job predicting individual movements, but they didn’t account for these important relationships between people. The new framework solves this problem by creating separate networks for understanding individual body parts (intra-relations) and interactions between people (inter-relations). This approach allows the model to better predict group movements. |
Keywords
» Artificial intelligence » Attention » Gcn
