A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/1905.11137v2.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
Learning to Detect and Retrieve Objects from Unlabeled Videos
[article]
<span title="2019-10-19">2019</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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<span title="2019-05-27">2019</span>
<span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1905.11137v1">arXiv:1905.11137v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/a3lrahw2xrdsbl2dpsbjhs574a">fatcat:a3lrahw2xrdsbl2dpsbjhs574a</a> </span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1905.11137v1">arXiv:1905.11137v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/a3lrahw2xrdsbl2dpsbjhs574a">fatcat:a3lrahw2xrdsbl2dpsbjhs574a</a> </span>
Learning an object detector or retrieval requires a large data set with manual annotations. Such data sets are expensive and time consuming to create and therefore difficult to obtain on a large scale. In this work, we propose to exploit the natural correlation in narrations and the visual presence of objects in video, to learn an object detector and retrieval without any manual labeling involved. We pose the problem as weakly supervised learning with noisy labels, and propose a novel object
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<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1905.11137v2">arXiv:1905.11137v2</a>
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... ection paradigm under these constraints. We handle the background rejection by using contrastive samples and confront the high level of label noise with a new clustering score. Our evaluation is based on a set of 11 manually annotated objects in over 5000 frames. We show comparison to a weakly-supervised approach as baseline and provide a strongly labeled upper bound.
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