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[2602.19022] An interpretable framework using foundation models for fish sex identification
Summary
The paper presents FishProtoNet, a non-invasive computer vision framework for accurately identifying the sex of delta smelt, an endangered fish species, enhancing aquaculture management.
Why It Matters
This research addresses critical challenges in aquaculture, particularly for endangered species like delta smelt, by providing a non-invasive method for sex identification. This can help optimize breeding strategies and contribute to conservation efforts, making it relevant for both environmental sustainability and technological advancement in computer vision.
Key Takeaways
- FishProtoNet offers a non-invasive method for fish sex identification.
- The framework utilizes foundation models to improve robustness against background noise.
- Achieves accuracy rates of 74.40% and 81.16% during different spawning stages.
- Provides interpretability through learned prototype representations.
- Source code is publicly available, promoting further research and application.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.19022 (cs) [Submitted on 22 Feb 2026] Title:An interpretable framework using foundation models for fish sex identification Authors:Zheng Miao, Tien-Chieh Hung View a PDF of the paper titled An interpretable framework using foundation models for fish sex identification, by Zheng Miao and 1 other authors View PDF Abstract:Accurate sex identification in fish is vital for optimizing breeding and management strategies in aquaculture, particularly for species at the risk of extinction. However, most existing methods are invasive or stressful and may cause additional mortality, posing severe risks to threatened or endangered fish populations. To address these challenges, we propose FishProtoNet, a robust, non-invasive computer vision-based framework for sex identification of delta smelt (Hypomesus transpacificus), an endangered fish species native to California, across its full life cycle. Unlike the traditional deep learning methods, FishProtoNet provides interpretability through learned prototype representations while improving robustness by leveraging foundation models to reduce the influence of background noise. Specifically, the FishProtoNet framework consists of three key components: fish regions of interest (ROIs) extraction using visual foundation model, feature extraction from fish ROIs and fish sex identification based on an interpretable prototype network. FishProtoNet demonstrates strong performance...
