Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

TL;DR

This paper introduces Decoupled Classification Refinement (DCR), a method that enhances object detector classification accuracy by focusing on hard false positives, leading to state-of-the-art results on PASCAL VOC and COCO datasets.

Contribution

The paper proposes a simple, effective DCR network that improves classification in object detection by decoupling classification from localization, addressing failure cases of existing detectors.

Findings

DCR significantly reduces false positives in object detection.

Achieves new state-of-the-art results on PASCAL VOC and COCO.

Decoupling classification improves detector performance without complex modifications.

Abstract

Recent region-based object detectors are usually built with separate classification and localization branches on top of shared feature extraction networks. 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. We conjecture that: (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.We demonstrate the potential of detector classification power by a simple, effective, and widely-applicable Decoupled Classification Refinement (DCR) network. DCR samples hard false positives from the base classifier in Faster RCNN and trains a RCNN-styled strong classifier. Experiments show new state-of-the-art results on PASCAL VOC and COCO without any bells and whistles.