{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/watermask-instance-segmentation-for","title":"WaterMask: Instance Segmentation for Underwater Imagery","arxiv_id":null,"date":"2023-01-01","proceeding":"ICCV 2023 1","authors":["Shijie Lian","Hua Li","Runmin Cong","Suqi Li","Wei zhang","Sam Kwong"],"abstract":"    Underwater image instance segmentation is a fundamental and critical step in underwater image analysis and understanding. However, the paucity of general multiclass instance segmentation datasets has impeded the development of instance segmentation studies for underwater images. In this paper, we propose the first underwater image instance segmentation dataset (UIIS), which provides 4628 images for 7 categories with pixel-level annotations. Meanwhile, we also design WaterMask for underwater image instance segmentation for the first time. In Water- Mask, we first devise Difference Similarity Graph Attention Module (DSGAT) to recover lost detailed information due to image quality degradation and downsampling to help the network prediction. Then, we propose Multi-level Feature Refinement Module (MFRM) to predict foreground masks and boundary masks separately by features at different scales, and guide the network through Boundary Mask Strategy (BMS) with boundary learning loss to provide finer prediction results. Extensive experimental results demonstrates that WaterMask can achieve significant gains of 2.9, 3.8 mAP over Mask R-CNN when using ResNet-50 and ResNet-101. Code and Dataset are available at https: //github.com/LiamLian0727/WaterMask.    ","url_abs":"http://openaccess.thecvf.com//content/ICCV2023/html/Lian_WaterMask_Instance_Segmentation_for_Underwater_Imagery_ICCV_2023_paper.html","url_pdf":"http://openaccess.thecvf.com//content/ICCV2023/papers/Lian_WaterMask_Instance_Segmentation_for_Underwater_Imagery_ICCV_2023_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"watermask-instance-segmentation-for","repo_url":"https://github.com/liamlian0727/watermask","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"2d-object-detection","task_name":"2D Object Detection"},{"task_slug":"graph-attention","task_name":"Graph Attention"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"mask-r-cnn","method_name":"Mask R-CNN"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"roi-align","method_name":"RoIAlign"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[{"slug":"uiis-dataset","name":"UIIS","full_name":"General Underwater Image Instance Segmentation dataset"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/instance-segmentation-on-uiis","task":"Instance Segmentation","dataset":"UIIS","model":"WaterMask RCNN","rank_in_archive_order":1,"of":2,"metrics":{"Prams（M）":"67","mAP":"27.2"},"uses_additional_data":false},{"leaderboard":"/sota/instance-segmentation-on-uiis","task":"Instance Segmentation","dataset":"UIIS","model":"Cascade WaterMask R-CNN","rank_in_archive_order":2,"of":2,"metrics":{"Prams（M）":"107","mAP":"27.1"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}