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In this paper, we investigate two new strategies to detect objects accurately and efficiently using deep convolutional neural network: 1) scale-dependent pooling and 2) layerwise cascaded rejection classifiers. The scale-dependent pooling (SDP) improves detection accuracy by exploiting appropriate convolutional features depending on the scale of candidate object proposals. The cascaded rejection classifiers (CRC) effectively utilize convolutional features and eliminate negative object proposalsdoi:10.1109/cvpr.2016.234 dblp:conf/cvpr/YangCL16 fatcat:7h7ry3dgonaxtnsypvsr52rc2e