Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection

5 Oct 2018Bowen ChengYunchao WeiHonghui ShiRogerio FerisJinjun XiongThomas Huang

In this paper, we analyze failure cases of state-of-the-art detectors and observe that most hard false positives result from classification instead of localization and they have a large negative impact on the performance of object detectors. We conjecture there are three factors: (1) Shared feature representation is not optimal due to the mismatched goals of feature learning for classification and localization; (2) multi-task learning helps, yet optimization of the multi-task loss may result in sub-optimal for individual tasks; (3) large receptive field for different scales leads to redundant context information for small objects... (read more)

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