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- W4297789608 abstract "The increasing prominence of weakly labeled data nurtures 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." @default.
- W4297789608 created "2022-10-01" @default.
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- W4297789608 date "2014-06-25" @default.
- W4297789608 modified "2023-09-25" @default.
- W4297789608 title "Weakly-supervised Discovery of Visual Pattern Configurations" @default.
- W4297789608 doi "https://doi.org/10.48550/arxiv.1406.6507" @default.
- W4297789608 hasPublicationYear "2014" @default.
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