Cross-Domain Human Action Recognition from Multiview Motion and Textual Descriptions

2026-05-21 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

A novel orientation-aware action recognition approach is presented to improve Zero-Shot Action Recognition (ZSAR) performance, particularly in scenarios with significant domain shifts from varying human body orientations and camera viewpoints. This method addresses a central challenge in ZSAR by combining motion cues from multiple camera viewpoints and textual descriptions of human actions during the training phase. It introduces a new orientation-aware motion encoding network to learn distinct motion features and adapts a specific orientation-aware text prompt to match these features at inference. Extensive experiments demonstrate that this proposed method consistently improves ZSAR performance across several recognition benchmarks, including NTU-RGB+D, BABEL, NW-UCLA, and two surveillance datasets, outperforming recent zero-shot approaches. Furthermore, the learned representations exhibit strong transfer learning capabilities, achieving competitive performance on both cross-domain and same-domain recognition of seen actions.

Key takeaway

For Computer Vision Engineers deploying Zero-Shot Action Recognition (ZSAR) systems, you should consider integrating orientation-aware models to mitigate performance degradation from varying human body orientations and camera viewpoints. This approach, which combines multiview motion and text descriptions, offers robust generalization across domain shifts. You can use the provided code and trained models to enhance your ZSAR capabilities in surveillance and other dynamic environments, improving recognition of both seen and novel actions.

Key insights

A new orientation-aware ZSAR method combines multiview motion and text descriptions to overcome domain shifts from varying human orientations and camera viewpoints.

Principles

• Domain shifts from orientation and viewpoint limit ZSAR.
• Combining multiview motion and text improves ZSAR.
• Orientation-aware encoding enhances cross-domain generalization.

Method

Train an orientation-aware motion encoding network with multiview motion and text descriptions. Adapt an orientation-aware text prompt to match learned features for inference, improving ZSAR across domain shifts.

In practice

• Deploy ZSAR in varied camera viewpoints.
• Enhance action recognition in surveillance.
• Improve generalization to unseen action-motion.

Topics

• Human Action Recognition
• Zero-Shot Action Recognition
• Cross-Domain Generalization
• Multiview Motion
• Textual Descriptions
• Orientation-Aware Models

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.