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We address an unsupervised object detection and segmentation problem that goes beyond the conventional assumptions of one-to-one object correspondences or model-test settings betwe...
Minsu Cho (Seoul National University), Young Min S...
We propose a novel step toward the unsupervised segmentation of whole objects by combining "hints" of partial scene segmentation offered by multiple soft, binary mattes....
Andrew N. Stein, Thomas S. Stepleton, Martial Hebe...
We present a new unsupervised method to learn unified probabilistic object models (POMs) which can be applied to classification, segmentation, and recognition. We formulate this a...
Yuanhao Chen, Long Zhu, Alan L. Yuille, HongJiang ...
In this paper we present a general framework for object detection and segmentation. Using a bottom-up unsupervised merging algorithm, a region-based hierarchy that represents the ...
We describe an unsupervised method to segment objects detected in images using a novel variant of an interest point template, which is very efficient to train and evaluate. Once a...
Himanshu Arora, Nicolas Loeff, David A. Forsyth, N...