Papers › Rethinking Classification and Localization for Object Detection

Rethinking Classification and Localization for Object Detection

13 Apr 2019CVPR 2020 6arXiv:1904.06493archive 2025-07-28

Yue Wu, Yinpeng Chen, Lu Yuan, Zicheng Liu, Lijuan Wang, Hongzhi Li, Yun Fu

Two head structures (i.e. fully connected head and convolution head) have been widely used in R-CNN based detectors for classification and localization tasks. However, there is a lack of understanding of how does these two head structures work for these two tasks. To address this issue, we perform a thorough analysis and find an interesting fact that the two head structures have opposite preferences towards the two tasks. Specifically, the fully connected head (fc-head) is more suitable for the classification task, while the convolution head (conv-head) is more suitable for the localization task. Furthermore, we examine the output feature maps of both heads and find that fc-head has more spatial sensitivity than conv-head. Thus, fc-head has more capability to distinguish a complete object from part of an object, but is not robust to regress the whole object. Based upon these findings, we propose a Double-Head method, which has a fully connected head focusing on classification and a convolution head for bounding box regression. Without bells and whistles, our method gains +3.5 and +2.8 AP on MS COCO dataset from Feature Pyramid Network (FPN) baselines with ResNet-50 and ResNet-101 backbones, respectively.

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wuyuebupt/doubleheadsrcnn officialpytorch report

open-mmlab/mmdetection pytorchApache-2.0 report

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Classification General Classification Object Object Detection object-detection

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1x1 Convolution Average Pooling Batch Normalization Bottleneck Residual Block Convolution FPN Global Average Pooling Kaiming Initialization Mask R-CNN Max Pooling RPN ReLU Residual Block Residual Connection RoIAlign Softmax

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