Title: Unsupervised discovery of objects using temporal coherence Speaker: Brandon Sanders Abstract: We present a novel unsupervised method for discovering objects in image sequences. Instead of using spatial homogeneity to partition pixels into regions, we group pixels into large temporally coherent clusters (TCCs) having a unique temporal signature. Each cluster's temporal signature is explained by hypothesizing the arrival and departure of a small set of objects. Explanations of less ambiguous clusters are used to disambiguate the explanations of their more complicated neighbors. The recovered objects and arrival/departure events explain each frame in the image sequence by specifying (1) which objects are present, (2) the ways in which they occlude each other, and (3) the pixels in the frame that are not part of the background or the objects. Our framework ignores distracting motion, correctly deals with occlusion (including mutual occlusion), and recovers entire objects even in cases where they are partially occluded in every frame. Because we do not use spatial information in our clustering steps the technique is significantly different from and complements traditional spatially based segmentation algorithms. The recovered 2D object masks are suitable for unsupervised training and initialization of object recognition and tracking systems.