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.
In: Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2022)
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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.