CA-SSD: Channel-Independent Rare Class Attention for 3D Object Detection

The current 3D object detection datasets suffer from a serve problem of uneven data distribution. In autonomous driving scenarios, rare classes are also critical. The existing methods perform poorly in these classes. Generic methods of solving long-tailed distributions such as re-sampling and re-weighting would over-fit or destroy the original distribution. The popular solution is to design detectors for different classes, which improves detection accuracy but reduces generalizability. Motived by this, we propose a simple and effective single-stage detector named CA-SSD, which can detect multiple categories simultaneously. The key to our approach is the channel transformation of the features, which corrects for the bias in task distribution from the training phase to the testing phase. In addition, to reduce the information loss due to multiple downsampling, a self-attention module is introduced to retain richer contextual information. Experimental results on the KITTI benchmark demonstrate the superiority of CA-SSD, especially on small samples. Besides, we designed ablation experiments to verify the effectiveness of both modules.