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New FSVL framework enhances fish identification with dual-path AI

Researchers have developed FSVL, a novel framework that combines fast and slow pathways for identifying fish species in the Middle Yangtze River Basin. The fast pathway, using ResNet50, achieves high preliminary accuracy, while a slow pathway with YOLOv8n refines identification for confusable species by analyzing key morphological regions. This system integrates with a Qwen3-14B language model for knowledge generation, significantly improving scientific name and genus accuracy, and ROUGE-L scores, aiding biodiversity monitoring and fisheries management. AI

IMPACT This framework could improve biodiversity monitoring and fisheries management through more accurate and efficient species identification.

RANK_REASON The item describes a new academic framework and its performance on a specific task, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New FSVL framework enhances fish identification with dual-path AI

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The item describes a new academic framework and its performance on a specific task, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Meng Liang, Fucheng Wang, Lang Zhao, Jun Chen, Ziying Li, Yuxiang Zhao, Haozhuang Chi, Wenkai Fang, Qiong Zhou, Junlin Yang, Jinming Wu ·

    FSVL: Taxonomic Fast-Slow Vision-to-Language Collaborative Framework for Interpretable Confusable Fish Species Identification and Knowledge Generation in the Middle Yangtze River

    arXiv:2610.31880v1 Announce Type: cross Abstract: The Yangtze River Basin is a critical region for freshwater biodiversity conservation. The implementation of the Yangtze River Ten-Year Fishing Ban has increased the importance of effective aquatic biodiversity monitoring and spec…