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arxiv:2506.13458
Leveraging Vision-Language Pre-training for Human Activity Recognition in Still Images
Published on Jun 16
Abstract
Contrastive vision-language pre-training significantly enhances the accuracy of action recognition in single photos compared to traditional CNNs.
AI-generated summary
Recognising human activity in a single photo enables indexing, safety and assistive applications, yet lacks motion cues. Using 285 MSCOCO images labelled as walking, running, sitting, and standing, scratch CNNs scored 41% accuracy. Fine-tuning multimodal CLIP raised this to 76%, demonstrating that contrastive vision-language pre-training decisively improves still-image action recognition in real-world deployments.
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