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

Ellie Zhou, Jihoon Chung, Olga Russakovsky

Keywords: Background Bias, Action Recognition, Multi-Modal LLM

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%.

Submission Type: Short Research Paper (< 4 Pages)

Submission Number: 8