Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: TubeDETR: Spatio-Temporal Video Grounding with Transformers   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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The categories of this paper are: q-alg
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-th
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
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The categories of this paper are: nlin
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The categories of this paper are: chao-dyn
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The categories of this paper are: astro-ph
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The categories of this paper are: adap-org
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The categories of this paper are: patt-sol
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The categories of this paper are: mtrl-th
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The categories of this paper are: stat
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The categories of this paper are: physics
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The categories of this paper are: cond-mat
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The categories of this paper are: eess