Seeing Beyond the Scene: Analyzing and Mitigating Background Bias in Action Recognition
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| Format: | Preprint |
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2025
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| _version_ | 1866917159472463872 |
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| author | Zhou, Ellie Chung, Jihoon Russakovsky, Olga |
| author_facet | Zhou, Ellie Chung, Jihoon Russakovsky, Olga |
| contents | Human action recognition models often rely on background cues rather than human movement and pose to make predictions, a behavior known as background bias. We present a systematic analysis of background bias across classification models, contrastive text-image pretrained models, and Video Large Language Models (VLLM) and find that all exhibit a strong tendency to default to background reasoning. Next, we propose mitigation strategies for classification models and show that incorporating segmented human input effectively decreases background bias by 3.78%. Finally, we explore manual and automated prompt tuning for VLLMs, demonstrating that prompt design can steer predictions towards human-focused reasoning by 9.85%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_17953 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Seeing Beyond the Scene: Analyzing and Mitigating Background Bias in Action Recognition Zhou, Ellie Chung, Jihoon Russakovsky, Olga Computer Vision and Pattern Recognition Artificial Intelligence Human action recognition models often rely on background cues rather than human movement and pose to make predictions, a behavior known as background bias. We present a systematic analysis of background bias across classification models, contrastive text-image pretrained models, and Video Large Language Models (VLLM) and find that all exhibit a strong tendency to default to background reasoning. Next, we propose mitigation strategies for classification models and show that incorporating segmented human input effectively decreases background bias by 3.78%. Finally, we explore manual and automated prompt tuning for VLLMs, demonstrating that prompt design can steer predictions towards human-focused reasoning by 9.85%. |
| title | Seeing Beyond the Scene: Analyzing and Mitigating Background Bias in Action Recognition |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2512.17953 |