Semantic Role Aware Correlation Transformer For Text To Video Retrieval

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Semantic Role Aware Correlation Transformer For Text To Video Retrieval
Title:
Semantic Role Aware Correlation Transformer For Text To Video Retrieval
Journal Title:
2021 IEEE International Conference on Image Processing (ICIP)
Keywords:
Publication Date:
23 August 2021
Citation:
Satar, B., Hongyuan, Z., Bresson, X., & Lim, J. H. (2021). Semantic Role Aware Correlation Transformer For Text To Video Retrieval. 2021 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip42928.2021.9506267
Abstract:
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 which 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 these three roles to discover discriminative features for matching at different levels. The preliminary results on popular YouCook2 indicate that our approach surpasses state-of-the-arts with a high margin.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - AME Programmatic Fund
Grant Reference no. : A18A2b0046
Description:
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
ISSN:
2381-8549
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