报告摘要：After a brief overview of computer vision research in my group (INRIA-willow), I will address in this presentation the unsupervised discovery and localization of dominant objects from a noisy collection of images or videos. The setting of this problem is fully unsupervised, without even class labels or any assumption of a single dominant class, and thus far more general than those of typical colocalization or weakly-supervised localization tasks. We tackle the discovery and localization problem using a part-based region matching approach.  For each image/frame, a dominant object is localized by comparing the scores of candidate regions and selecting those that stand out over other regions containing them. Given a video collection, we also associate similar object regions along consecutive frames within the same video, thus achieving unsupervised tracking. Extensive experimental evaluations on standard benchmarks demonstrate that the proposed approach substantially outperforms the current state of the art in colocalization, and achieves robust object discovery in challenging mixed-class datasets. 

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