Weakly-supervised Discovery of Visual Pattern Configurations

Part of Advances in Neural Information Processing Systems 27 (NIPS 2014)

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Hyun Oh Song, Yong Jae Lee, Stefanie Jegelka, Trevor Darrell


The prominence of weakly labeled data gives rise to a growing demand for object detection methods that can cope with minimal supervision. We propose an approach that automatically identifies discriminative configurations of visual patterns that are characteristic of a given object class. We formulate the problem as a constrained submodular optimization problem and demonstrate the benefits of the discovered configurations in remedying mislocalizations and finding informative positive and negative training examples. Together, these lead to state-of-the-art weakly-supervised detection results on the challenging PASCAL VOC dataset.