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: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2020)
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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.