With the emergence of social media, voluminous video clips are uploaded every day, and retrieving the most relevant visual content with a language query becomes critical. Most approaches aim to learn a joint embedding space for plain textual and visual contents without adequately exploiting their intra-modality structures and inter-modality correlations. This paper proposes a novel transformer that explicitly disentangles the text and video into semantic roles of objects, spatial contexts and temporal contexts with an attention scheme to learn the intra- and inter-role correlations among the three roles to discover discriminative features for matching at different levels. The preliminary results on popular YouCook2 indicate that our approach surpasses a current state-of-the-art method, with a high margin in all metrics. It also overpasses two SOTA methods in terms of two metrics.
@article{arxiv.2206.12849,
title = {Semantic Role Aware Correlation Transformer for Text to Video Retrieval},
author = {Burak Satar and Hongyuan Zhu and Xavier Bresson and Joo Hwee Lim},
journal= {arXiv preprint arXiv:2206.12849},
year = {2023}
}