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[2602.08755] Align and Adapt: Multimodal Multiview Human Activity Recognition under Arbitrary View Combinations
Summary
The paper presents AliAd, a model for multimodal multiview human activity recognition that enhances performance by integrating diverse view configurations and employing an innovative contrastive learning approach.
Why It Matters
This research addresses the challenges in human activity recognition by allowing for flexible view combinations, which is crucial for real-world applications where view availability can vary. The proposed model not only improves recognition accuracy but also reduces computational complexity, making it a significant advancement in the field of machine learning.
Key Takeaways
- AliAd model integrates multiview contrastive learning with a mixture-of-experts module.
- The adjusted center contrastive loss enhances self-supervised representation learning.
- Computational complexity is reduced from O(V^2) to O(V), improving efficiency.
- The model is validated on multiple datasets, demonstrating flexibility and performance.
- Addresses the issue of missing views in human activity recognition effectively.
Computer Science > Machine Learning arXiv:2602.08755 (cs) [Submitted on 9 Feb 2026 (v1), last revised 18 Feb 2026 (this version, v3)] Title:Align and Adapt: Multimodal Multiview Human Activity Recognition under Arbitrary View Combinations Authors:Duc-Anh Nguyen, Nhien-An Le-Khac View a PDF of the paper titled Align and Adapt: Multimodal Multiview Human Activity Recognition under Arbitrary View Combinations, by Duc-Anh Nguyen and 1 other authors View PDF HTML (experimental) Abstract:Multimodal multiview learning seeks to integrate information from diverse sources to enhance task performance. Existing approaches often struggle with flexible view configurations, including arbitrary view combinations, numbers of views, and heterogeneous modalities. Focusing on the context of human activity recognition, we propose AliAd, a model that combines multiview contrastive learning with a mixture-of-experts module to support arbitrary view availability during both training and inference. Instead of trying to reconstruct missing views, an adjusted center contrastive loss is used for self-supervised representation learning and view alignment, mitigating the impact of missing views on multiview fusion. This loss formulation allows for the integration of view weights to account for view quality. Additionally, it reduces computational complexity from $O(V^2)$ to $O(V)$, where $V$ is the number of views. To address residual discrepancies not captured by contrastive learning, we employ a mixtu...
