  Self-attention is one of the most successful designs in deep learning, which
calculates the similarity of different tokens and reconstructs the feature
based on the attention matrix. Originally designed for NLP, self-attention is
also popular in computer vision, and can be categorized into pixel-level
attention and patch-level attention. In object detection, RoI features can be
seen as patches from base feature maps. This paper aims to apply the attention
module to RoI features to improve performance. Instead of employing an original
self-attention module, we choose the external attention module, a modified
self-attention with reduced parameters. With the proposed double head structure
and the Positional Encoding module, our method can achieve promising
performance in object detection. The comprehensive experiments show that it
achieves promising performance, especially in the underwater object detection
dataset. The code will be avaiable in:
https://github.com/zsyasd/Excavating-RoI-Attention-for-Underwater-Object-Detection
