VicTR: Video-conditioned Text Representations for Activity Recognition

Vision-Language models have shown strong performance in the image-domain -- even in zero-shot settings, thanks to the availability of large amount of pretraining data (i.e., paired image-text examples). However for videos, such paired data is not as abundant. Thus, video-text models are usually designed by adapting pretrained image-text models to video-domain, instead of training from scratch. All such recipes rely on augmenting visual embeddings with temporal information (i.e., image -> video), often keeping text embeddings unchanged or even being discarded. In this paper, we argue that such adapted video-text models can benefit more by augmenting text rather than visual information. We propose VicTR, which jointly-optimizes text and video tokens, generating 'Video-conditioned Text' embeddings. Our method can further make use of freely-available semantic information, in the form of visually-grounded auxiliary text (e.g., object or scene information). We conduct experiments on multiple benchmarks including supervised (Kinetics-400, Charades), zero-shot and few-shot (HMDB-51, UCF-101) settings, showing competitive performance on activity recognition based on video-text models.