Training CLIP-based action recognition models with masked backgrounds or objects lowers their reliance on static scene cues and improves person-focused accuracy on several video datasets.
IE- ICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E104.A(6), 846–856 (2021)
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Can masking background and object reduce static bias for zero-shot action recognition?
Training CLIP-based action recognition models with masked backgrounds or objects lowers their reliance on static scene cues and improves person-focused accuracy on several video datasets.