mlsanityData to 14 Oct 2026

Excavating RoI Attention for Underwater Object Detection

arXiv 2206.12128 ↗First posted 24 Jun 2022cs.CVcited by 7

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Abstract (arXiv)

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

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 26 of its 30 references that are in the map. First posted on arXiv 24 Jun 2022.

Builds on

26 of 30 references.

References · 26

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14 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 4; 2024: 1; 2025: 1.

7 papers.

  1. 2022Boosting R-CNN: Reweighting R-CNN Samples by RPN's Error for Underwater Object Detectioncited by 10
  2. 2024Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Futurecited by 8
  3. 2023A Gated Cross-domain Collaborative Network for Underwater Object Detectioncited by 5
  4. 2023UnitModule: A Lightweight Joint Image Enhancement Module for Underwater Object Detectioncited by 5
  5. 2023Semantic-Aware Transformation-Invariant RoI Aligncited by 1
  6. 2025Underwater Diffusion Attention Network with Contrastive Language-Image Joint Learning for Underwater Image Enhancementcited by 1
  7. 2023Domain Similarity-Perceived Label Assignment for Domain Generalized Underwater Object Detectioncited by 0