Representation Reconstruction Head For Object Detection

There are two kinds of detection heads in object detection frameworks. Between them, the heads based on full connection contribute to mapping the learned feature representation to the sample label space, while the heads based on full convolution facilitate preserving location sensitivity information. However, to enjoy the benefits from both detection heads is still underexplored. In this paper, we propose a generalized Representation Reconstruction Head (RRHead) to break through the limitation that most detection heads focus on unilateral self-advantage while ignoring another one. RRHead enhances multimodal feature representation for better feature mapping, and employs location sensitivity representation for better location preservation. These optimize fully-convolutional-based heads and fully-connected-based heads separately. RRHead can be embedded in existing detection frameworks to heighten the rationality and reliability of the detection head representation without any additional modification. Extensive experiments show that our proposed RRHead improves the detection performance of the existing frameworks by a large margin on several challenging benchmarks, and achieves new state-of-the-art performance.