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<title>Spacewalk-18</title>
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<h1 class="title is-2 publication-title">Spacewalk-18: A Benchmark for Multimodal and Long-form Procedural Video Understanding</h1>
<div class="is-size-5 publication-authors">
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<a href="https://scholar.google.com/citations?user=koxiPYIAAAAJ&hl=en&oi=sra" target="_blank">Rohan Myer Krishnan</a><sup>*</sup>,</span>
<span class="author-block">
<a href="https://zitiantang.github.io/" target="_blank">Zitian Tang</a><sup>*</sup>,</span>
<span class="author-block">
<a href="https://brown-palm.github.io/Spacewalk-18" target="_blank">Zhiqiu Yu</a>
</span>,
<span class="author-block">
<a href="https://chensun.me/" target="_blank">Chen Sun</a>
</span>
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<span class="author-block">Brown University<!--<br>Conferance name and year--></span>
<span class="eql-cntrb"><small><br><sup>*</sup>Indicates Equal Contribution</small></span>
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<h2 class="title is-3">Abstract</h2>
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<p>
Learning from videos is an emerging research area that enables robots to acquire skills from human demonstrations, such as procedural videos. To do this, video-language models must be able to obtain structured understandings, such as the temporal segmentation of a demonstration into sequences of actions and skills, and to generalize the understandings to novel domains. In pursuit of this goal, we introduce Spacewalk-18, a benchmark containing two tasks: (1) step recognition and (2) intra-video retrieval over a dataset of temporally segmented and labeled tasks in International Space Station spacewalk recordings. In tandem, the two tasks quantify a model's ability to make use of: (1) out-of-domain visual information; (2) a high temporal context window; and (3) multimodal (e.g. visual and speech) domains. This departs from existing benchmarks for procedural video understanding, which typically deal with short context lengths and can be solved with a single modality. Spacewalk-18, with its inherent multimodal and long-form complexity, exposes the high difficulty of task recognition and segmentation. We find that state-of-the-art methods perform poorly on our benchmark, but improvements can be obtained by incorporating information from longer-range temporal context across different modalities. Our experiments underscore the need to develop new approaches to these tasks.
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<h2 class="title is-3">Spacewalk-18 Dataset</h2>
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<img src="static/videos/teaser.gif" alt="teaser"/>
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<h2 class="title is-3">Statistics</h2>
<p>Spacewalk-18 annotates 18 spacewalk videos from 2019 to 2023 with a total length of 96 hours. On average, each spacewalk task consists of 25 steps. Each step has an average of 12 minutes of animation video. </p>
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Distribution of shot durations
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<h2 class="title is-3">Tasks</h2>
<p>Two multimodal long-form video understanding tasks are defined on Spacewalk-18 - <b> step recognition </b> and <b> intra-video retrieval</b>.</p>
<br>
<p><b> Step recognition.</b> Given a timestamp and a context window length, step recognition aims to recognize the task step that the timestamp belongs to.</p>
<br>
<p><b> Intra-video retrieval.</b> Given a query timestamp, two candidate timestamps with the same time distances to the query, and a context window length, intra-video retrieval aims to determine the candidate that belongs to the same task step as the query.</p>
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<!--End Tasks -->
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<h2 class="title is-3">Evaluation Results</h2>
<p>We evaluate a few pretrained models in both zero-shot and fine-tuning scenarios. All the models perform poorly on our tasks and significantly worse than humans.</p>
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<h2 class="subtitle has-text-centered">
Evaluation on step recognition task
</h2>
</div>
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<img src="static/images/table_retrieval.png" alt="retrieval"/>
<h2 class="subtitle has-text-centered">
Evaluation on intra-video retrieval task
</h2>
</div>
</div>
</div>
</div>
</section>
<!-- End Evaluations-->
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<!--BibTex citation -->
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@misc{krishnan2023spacewalk18,
title={Spacewalk-18: A Benchmark for Multimodal and Long-form Procedural Video Understanding in Novel Domains},
author={Rohan Myer Krishnan and Zitian Tang and Zhiqiu Yu and Chen Sun},
year={2023},
eprint={2311.18773},
archivePrefix={arXiv},
primaryClass={cs.CV}
}</code></pre>
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</section>
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