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• W3016163669 startingPage "3632" @default.
• W3016163669 abstract "We present an approach for jointly matching and segmenting object instances of the same category within a collection of images. In contrast to existing algorithms that tackle the tasks of semantic matching and object co-segmentation in isolation, our method exploits the complementary nature of the two tasks. The key insights of our method are two-fold. First, the estimated dense correspondence fields from semantic matching provide supervision for object co-segmentation by enforcing consistency between the predicted masks from a pair of images. Second, the predicted object masks from object co-segmentation in turn allow us to reduce the adverse effects due to background clutters for improving semantic matching. Our model is end-to-end trainable and does not require supervision from manually annotated correspondences and object masks. We validate the efficacy of our approach on five benchmark datasets: TSS, Internet, PF-PASCAL, PF-WILLOW, and SPair-71k, and show that our algorithm performs favorably against the state-of-the-art methods on both semantic matching and object co-segmentation tasks." @default.
• W3016163669 created "2020-04-17" @default.
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• W3016163669 date "2021-10-01" @default.
• W3016163669 modified "2023-10-17" @default.
• W3016163669 title "Show, Match and Segment: Joint Weakly Supervised Learning of Semantic Matching and Object Co-Segmentation" @default.
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• W3016163669 doi "https://doi.org/10.1109/tpami.2020.2985395" @default.
• W3016163669 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32275584" @default.
• W3016163669 hasPublicationYear "2021" @default.
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