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arXiv · 2204.02547

Modeling Motion with Multi-Modal Features for Text-Based Video Segmentation

Wangbo Zhao·Kai Wang·Xiangxiang Chu·Fuzhao Xue·Xinchao Wang·Yang You
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Abstract

Text-based video segmentation aims to segment the target object in a video based on a describing sentence. Incorporating motion information from optical flow maps with appearance and linguistic modalities is crucial yet has been largely ignored by previous work. In this paper, we design a method to fuse and align appearance, motion, and linguistic features to achieve accurate segmentation. Specifically, we propose a multi-modal video transformer, which can fuse and aggregate multi-modal and temporal features between frames. Furthermore, we design a language-guided feature fusion module to progressively fuse appearance and motion features in each feature level with guidance from linguistic features. Finally, a multi-modal alignment loss is proposed to alleviate the semantic gap between features from different modalities. Extensive experiments on A2D Sentences and J-HMDB Sentences verify the performance and the generalization ability of our method compared to the state-of-the-art methods.

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Wangbo Zhao, Kai Wang, Xiangxiang Chu, Fuzhao Xue, Xinchao Wang, Yang You. 2022-04-06. Modeling Motion with Multi-Modal Features for Text-Based Video Segmentation. https://arxiv.org/abs/2204.02547

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