Collaborative Three-Stream Transformers for Video Captioning
This work addresses the problem of generating accurate captions for videos, which is important for applications in accessibility and video analysis, though it is incremental in improving existing transformer-based methods.
The paper tackles video captioning by modeling subject, predicate, and object separately using a three-stream transformer framework, achieving state-of-the-art performance on datasets like YouCookII, ActivityNet Captions, and MSVD.
As the most critical components in a sentence, subject, predicate and object require special attention in the video captioning task. To implement this idea, we design a novel framework, named COllaborative three-Stream Transformers (COST), to model the three parts separately and complement each other for better representation. Specifically, COST is formed by three branches of transformers to exploit the visual-linguistic interactions of different granularities in spatial-temporal domain between videos and text, detected objects and text, and actions and text. Meanwhile, we propose a cross-granularity attention module to align the interactions modeled by the three branches of transformers, then the three branches of transformers can support each other to exploit the most discriminative semantic information of different granularities for accurate predictions of captions. The whole model is trained in an end-to-end fashion. Extensive experiments conducted on three large-scale challenging datasets, i.e., YouCookII, ActivityNet Captions and MSVD, demonstrate that the proposed method performs favorably against the state-of-the-art methods.