Learning Trajectory-Word Alignments for Video-Language Tasks

Aligning objects with words plays a critical role in Image-Language BERT (IL-BERT) and Video-Language BERT (VDL-BERT). Different from the image case where an object covers some spatial patches, an object in a video usually appears as an object trajectory, i.e., it spans over a few spatial but longer temporal patches and thus contains abundant spatiotemporal contexts. However, modern VDL-BERTs neglect this trajectory characteristic that they usually follow IL-BERTs to deploy the patch-to-word (P2W) attention while such attention may over-exploit trivial spatial contexts and neglect significant temporal contexts. To amend this, we propose a novel TW-BERT to learn Trajectory-Word alignment for solving video-language tasks. Such alignment is learned by a newly designed trajectory-to-word (T2W) attention. Besides T2W attention, we also follow previous VDL-BERTs to set a word-to-patch (W2P) attention in the cross-modal encoder. Since T2W and W2P attentions have diverse structures, our cross-modal encoder is asymmetric. To further help this asymmetric cross-modal encoder build robust vision-language associations, we propose a fine-grained ``align-before-fuse'' strategy to pull close the embedding spaces calculated by the video and text encoders. By the proposed strategy and T2W attention, our TW-BERT achieves SOTA performances on text-to-video retrieval tasks, and comparable performances on video question answering tasks with some VDL-BERTs trained on much more data. The code will be available in the supplementary material.