ViGT: Proposal-free Video Grounding with Learnable Token in Transformer

• URL: http://arxiv.org/abs/2308.06009v1
• Date: Fri, 11 Aug 2023 08:30:08 GMT
• Title: ViGT: Proposal-free Video Grounding with Learnable Token in Transformer
• Authors: Kun Li, Dan Guo, Meng Wang
• Abstract summary: Video grounding task aims to locate queried action or event in an untrimmed video based on rich linguistic descriptions. Existing proposal-free methods are trapped in complex interaction between video and query. We propose a novel boundary regression paradigm that performs regression token learning in a transformer.
• Score: 28.227291816020646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The video grounding (VG) task aims to locate the queried action or event in an untrimmed video based on rich linguistic descriptions. Existing proposal-free methods are trapped in complex interaction between video and query, overemphasizing cross-modal feature fusion and feature correlation for VG. In this paper, we propose a novel boundary regression paradigm that performs regression token learning in a transformer. Particularly, we present a simple but effective proposal-free framework, namely Video Grounding Transformer (ViGT), which predicts the temporal boundary using a learnable regression token rather than multi-modal or cross-modal features. In ViGT, the benefits of a learnable token are manifested as follows. (1) The token is unrelated to the video or the query and avoids data bias toward the original video and query. (2) The token simultaneously performs global context aggregation from video and query features. First, we employed a sharing feature encoder to project both video and query into a joint feature space before performing cross-modal co-attention (i.e., video-to-query attention and query-to-video attention) to highlight discriminative features in each modality. Furthermore, we concatenated a learnable regression token [REG] with the video and query features as the input of a vision-language transformer. Finally, we utilized the token [REG] to predict the target moment and visual features to constrain the foreground and background probabilities at each timestamp. The proposed ViGT performed well on three public datasets: ANet Captions, TACoS and YouCookII. Extensive ablation studies and qualitative analysis further validated the interpretability of ViGT.

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