IA2U: A Transfer Plugin with Multi-Prior for In-Air Model to Underwater
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Abstract (arXiv)
In underwater environments, variations in suspended particle concentration and turbidity cause severe image degradation, posing significant challenges to image enhancement (IE) and object detection (OD) tasks. Currently, in-air image enhancement and detection methods have made notable progress, but their application in underwater conditions is limited due to the complexity and variability of these environments. Fine-tuning in-air models saves high overhead and has more optional reference work than building an underwater model from scratch. To address these issues, we design a transfer plugin with multiple priors for converting in-air models to underwater applications, named IA2U. IA2U enables efficient application in underwater scenarios, thereby improving performance in Underwater IE and OD. IA2U integrates three types of underwater priors: the water type prior that characterizes the degree of image degradation, such as color and visibility; the degradation prior, focusing on differences in details and textures; and the sample prior, considering the environmental conditions at the time of capture and the characteristics of the photographed object. Utilizing a Transformer-like structure, IA2U employs these priors as query conditions and a joint task loss function to achieve hierarchical enhancement of task-level underwater image features, therefore considering the requirements of two different tasks, IE and OD. Experimental results show that IA2U combined with an in-air model can achieve superior performance in underwater image enhancement and object detection tasks. The code will be made publicly available.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 43 references that are in the map. First posted on arXiv 12 Dec 2023.
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References · 26
- 2017Attention Is All You Needcited by 60,361
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,627
- 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,577
- 2022Conditional Prompt Learning for Vision-Language Modelscited by 1,433
- 2019MMDetection: Open MMLab Detection Toolbox and Benchmarkcited by 1,385
- 2017Cascade R-CNN: Delving into High Quality Object Detectioncited by 1,276
- 2022Simple Baselines for Image Restorationcited by 708
14 more
- 2019Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selectioncited by 540
- 2019FFA-Net: Feature Fusion Attention Network for Single Image Dehazingcited by 307
- 2019An Underwater Image Enhancement Benchmark Dataset and Beyondcited by 253
- 2021TOOD: Task-aligned One-stage Object Detectioncited by 218
- 2020Probabilistic Anchor Assignment with IoU Prediction for Object Detectioncited by 150
- 2021Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embeddingcited by 134
- 2023Contrastive Semi-supervised Learning for Underwater Image Restoration via Reliable Bankcited by 71
- 2022Underwater Ranker: Learn Which Is Better and How to Be Bettercited by 67
- 2023Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancementcited by 57
- 2023Scale-Aware Modulation Meet Transformercited by 49
- 2021Shallow-UWnet : Compressed Model for Underwater Image Enhancementcited by 35
- 2022SGUIE-Net: Semantic Attention Guided Underwater Image Enhancement with Multi-Scale Perceptioncited by 33
- 2023Five A+ Network: You Only Need 9K Parameters for Underwater Image Enhancementcited by 22
- 2023FeatEnHancer: Enhancing Hierarchical Features for Object Detection and Beyond Under Low-Light Visioncited by 22
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