This paper addresses the problem of automatically localizing dominant objects as spatio-temporal tubes in a noisy collection of videos with minimal or even no supervision. We formulate the problem as a combination of two complementary processes: discovery and tracking. The first one establishes correspondences between prominent regions across videos, and the second one associates similar object regions within the same video. Interestingly, our algorithm also discovers the implicit topology of frames associated with instances of the same object class across different videos, a role normally left to supervisory information in the form of class labels in conventional image and video understanding methods. Indeed, as demonstrated by our experiments, our method can handle video collections featuring multiple object classes, and substantially outperforms the state of the art in colocalization, even though it tackles a broader problem with much less supervision. * WILLOW project-team, Département d'Informatique de l'Ecole Normale Supérieure, ENS/Inria/CNRS UMR 8548. † LEAR project-team, Inria Grenoble Rhône-Alpes, Laboratoire Jean Kuntzmann, CNRS, Univ. Grenoble Alpes, France. dƌĂĐŬŝŶŐ KďũĞĐƚ ǁŝƚŚŝŶ sŝĚĞŽ 1550-5499/15 $31.00
Kwak et al. REF formulate the problem of unsupervised spatiotemporal object localization as a combination of discovery and tracking.
Unsupervised Object Discovery and Tracking in Video Collections