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• W749296795 abstract "A novel efficient method for extraction of object proposals is introduced. Its objectness function exploits deep spatial pyramid features, a novel fast-to-compute HoG-based edge statistic and the EdgeBoxes score [42]. The efficiency is achieved by the use of spatial bins in a novel combination with sparsity-inducing group normalized SVM. State-of-the-art recall performance is achieved on Pascal VOC07, significantly outperforming methods with comparable speed. Interestingly, when only 100 proposals per image are considered the method attains 78 % recall on VOC07. The method improves mAP of the RCNN class-specific detector, increasing it by 10 points when only 50 proposals are used in each image. The system trained on twenty classes performs well on the two hundred class ILSVRC2013 set confirming generalization capability." @default.
• W749296795 created "2016-06-24" @default.
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• W749296795 date "2015-12-01" @default.
• W749296795 modified "2023-10-17" @default.
• W749296795 title "Cascaded Sparse Spatial Bins for Efficient and Effective Generic Object Detection" @default.
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• W749296795 doi "https://doi.org/10.1109/iccv.2015.137" @default.
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