TubeDETR: Spatio-Temporal Video Grounding with Transformers

• URL: http://arxiv.org/abs/2203.16434v1
• Date: Wed, 30 Mar 2022 16:31:49 GMT
• Title: TubeDETR: Spatio-Temporal Video Grounding with Transformers
• Authors: Antoine Yang, Antoine Miech, Josef Sivic, Ivan Laptev, Cordelia Schmid
• Abstract summary: We consider the problem of encoder localizing a-temporal tube in a video corresponding to a given text query. To address this task, we propose TubeDETR, a transformer- architecture inspired by the recent success of such models for text-conditioned object detection.
• Score: 89.71617065426146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider the problem of localizing a spatio-temporal tube in a video corresponding to a given text query. This is a challenging task that requires the joint and efficient modeling of temporal, spatial and multi-modal interactions. To address this task, we propose TubeDETR, a transformer-based architecture inspired by the recent success of such models for text-conditioned object detection. Our model notably includes: (i) an efficient video and text encoder that models spatial multi-modal interactions over sparsely sampled frames and (ii) a space-time decoder that jointly performs spatio-temporal localization. We demonstrate the advantage of our proposed components through an extensive ablation study. We also evaluate our full approach on the spatio-temporal video grounding task and demonstrate improvements over the state of the art on the challenging VidSTG and HC-STVG benchmarks. Code and trained models are publicly available at https://antoyang.github.io/tubedetr.html.

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