Leveraging Vision-Language Pre-training for Human Activity Recognition in Still Images
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915346680643584 |
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| author | Mahanta, Cristina Bhatia, Gagan |
| author_facet | Mahanta, Cristina Bhatia, Gagan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_13458 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Leveraging Vision-Language Pre-training for Human Activity Recognition in Still Images Mahanta, Cristina Bhatia, Gagan Computer Vision and Pattern Recognition Computation and Language 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. |
| title | Leveraging Vision-Language Pre-training for Human Activity Recognition in Still Images |
| topic | Computer Vision and Pattern Recognition Computation and Language |
| url | https://arxiv.org/abs/2506.13458 |