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arXiv · 2512.17953

Seeing Beyond the Scene: Analyzing and Mitigating Background Bias in Action Recognition

Ellie Zhou·Jihoon Chung·Olga Russakovsky
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Abstract

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%.

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Ellie Zhou, Jihoon Chung, Olga Russakovsky. 2025-12-17. Seeing Beyond the Scene: Analyzing and Mitigating Background Bias in Action Recognition. https://arxiv.org/abs/2512.17953

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