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• W2214606977 abstract "Exploiting contextual relationships across images has recently proven key to improve object detection. The resulting object co-detection algorithms, however, fail to exploit the correlations between multiple classes and, for scalability reasons are limited to modeling object instance similarity with relatively low-dimensional hand-crafted features. Here, we address the problem of multiclass object co-detection for large scale datasets. To this end, we formulate co-detection as the joint multiclass labeling of object candidates obtained in a class-independent manner. To exploit the correlations between objects, we build a fully-connected CRF on the candidates, which explicitly incorporates both geometric layout relations across object classes and similarity relations across multiple images. We then introduce a structural boosting algorithm that lets us exploits rich, high-dimensional deep network features to learn object similarity within our fully-connected CRF. Our experiments on PASCAL VOC 2007 and 2012 evidences the benefits of our approach over object detection with RCNN, single-image CRF methods and state-of-the-art co-detection algorithms." @default.
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• W2214606977 date "2015-12-01" @default.
• W2214606977 modified "2023-10-02" @default.
• W2214606977 title "Structural Kernel Learning for Large Scale Multiclass Object Co-detection" @default.
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• W2214606977 doi "https://doi.org/10.1109/iccv.2015.302" @default.
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