Tagging before Alignment: Integrating Multi-Modal Tags for Video-Text Retrieval

• URL: http://arxiv.org/abs/2301.12644v1
• Date: Mon, 30 Jan 2023 03:53:19 GMT
• Title: Tagging before Alignment: Integrating Multi-Modal Tags for Video-Text Retrieval
• Authors: Yizhen Chen, Jie Wang, Lijian Lin, Zhongang Qi, Jin Ma, Ying Shan
• Abstract summary: Vision-language alignment learning for video-text retrieval arouses a lot of attention in recent years. In this paper, we integrate multi-modal information in an explicit manner by tagging, and use the tags as the anchors for better video-text alignment. To strengthen the interaction between video and text, we build a joint cross-modal encoder with the triplet input of [vision, tag, text] and perform two additional supervised tasks.
• Score: 23.418120617544545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-language alignment learning for video-text retrieval arouses a lot of attention in recent years. Most of the existing methods either transfer the knowledge of image-text pretraining model to video-text retrieval task without fully exploring the multi-modal information of videos, or simply fuse multi-modal features in a brute force manner without explicit guidance. In this paper, we integrate multi-modal information in an explicit manner by tagging, and use the tags as the anchors for better video-text alignment. Various pretrained experts are utilized for extracting the information of multiple modalities, including object, person, motion, audio, etc. To take full advantage of these information, we propose the TABLE (TAgging Before aLignmEnt) network, which consists of a visual encoder, a tag encoder, a text encoder, and a tag-guiding cross-modal encoder for jointly encoding multi-frame visual features and multi-modal tags information. Furthermore, to strengthen the interaction between video and text, we build a joint cross-modal encoder with the triplet input of [vision, tag, text] and perform two additional supervised tasks, Video Text Matching (VTM) and Masked Language Modeling (MLM). Extensive experimental results demonstrate that the TABLE model is capable of achieving State-Of-The-Art (SOTA) performance on various video-text retrieval benchmarks, including MSR-VTT, MSVD, LSMDC and DiDeMo.

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