Learning to Detect and Retrieve Objects from Unlabeled Videos [article]

Elad Amrani, Rami Ben-Ari, Tal Hakim, Alex Bronstein
<span title="2019-10-19">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</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
more &raquo; ... 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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