Papers › B2C-AFM: Bi-Directional Co-Temporal and Cross-Spatial Attention Fusion Model for Human...
B2C-AFM: Bi-Directional Co-Temporal and Cross-Spatial Attention Fusion Model for Human Action Recognition
Fangtai Guo, Tianlei Jin, Shiqiang Zhu, Xiangming Xi, Wen Wang, Qiwei Meng, Wei Song, and Jiakai Zhu
Human Action Recognition plays a driving engine of many human-computer interaction applications. Most current researches focus on improving the model generalization by integrating multiple homogeneous modalities, including RGB images, human poses, and optical flows. Furthermore, contextual interactions and out-of-context sign languages have been validated to depend on scene category and human per se. Those attempts to integrate appearance features and human poses have shown positive results. However, with human poses’ spatial errors and temporal ambiguities, existing methods are subject to poor scalability, limited robustness, and sub-optimal models. In this paper, inspired by the assumption that different modalities may maintain temporal consistency and spatial complementarity, we present a novel Bi-directional Co-temporal and Cross-spatial Attention Fusion Model (B2C-AFM). Our model is characterized by the asynchronous fusion strategy of multi-modal features along temporal and spatial dimensions. Besides, the novel explicit motion-oriented pose representations called Limb Flow Fields (Lff) are explored to alleviate the temporal ambiguity regarding human poses. Experiments on publicly available datasets validate our contributions. Abundant ablation studies experimentally show that B2C-AFM achieves robust performance across seen and unseen human actions. The codes are available at https://github.com/gftww/B2C.git.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Action Recognition | NTU RGB+D | B2C-AFM(RGB+Pose) | Accuracy (CS) | 91.7 | #24 of 28 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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