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• W2298532145 abstract "Given a set of images containing objects from the same category, the task of image co-localization is to identify and localize each instance. This paper shows that this problem can be solved by a simple but intriguing idea, that is, a common object detector can be learnt by making its detection confidence scores distributed like those of a strongly supervised detector. More specifically, we observe that given a set of object proposals extracted from an image that contains the object of interest, an accurate strongly supervised object detector should give high scores to only a small minority of proposals, and low scores to most of them. Thus, we devise an entropy-based objective function to enforce the above property when learning the common object detector. Once the detector is learnt, we resort to a segmentation approach to refine the localization. We show that despite its simplicity, our approach outperforms state-of-the-arts." @default.
• W2298532145 created "2016-06-24" @default.
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• W2298532145 date "2016-01-01" @default.
• W2298532145 modified "2023-10-02" @default.
• W2298532145 title "Image Co-localization by Mimicking a Good Detector’s Confidence Score Distribution" @default.
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• W2298532145 doi "https://doi.org/10.1007/978-3-319-46475-6_2" @default.
• W2298532145 hasPublicationYear "2016" @default.
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