Advances in Action Recognition
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 7
Special Issue Editor
Special Issue Information
Dear Colleagues,
Vision–language models (VLMs) have emerged as a promising direction for advancing human action recognition by combining visual data with natural language. Unlike traditional models that rely solely on appearance and motion cues, VLMs incorporate semantic information from text, enabling more interpretable and generalizable action understanding.
By aligning video features with textual descriptions, VLMs can better distinguish fine-grained or ambiguous actions, making them effective in scenarios with limited or noisy annotations. Pretrained models like CLIP and BLIP have demonstrated strong performance in zero-shot and few-shot settings, allowing action recognition systems to handle unseen classes and real-world variability without extensive retraining.
Recent work focuses on adapting these models to video data, addressing challenges such as temporal alignment and cross-modal reasoning. Approaches include prompt learning, contrastive training, and multimodal fusion to bridge the gap between static image–language models and dynamic video content.
As the field evolves, VLMs offer a flexible framework for building more robust, scalable, and semantically aware action recognition systems. This Special Issue seeks innovative contributions that explore new VLM architectures, learning methods, adaptation methods, efficient training methods, action-based human–robot interaction, and applications to push the boundaries of action recognition in diverse and complex environments.
Dr. Kunyu Peng
Guest Editor
Manuscript Submission Information
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Keywords
- vision–language model
- action recognition
- video understanding
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