2026-05-27 合肥物質科学研究院（HFIPS）
Feature Map Visualization of Different Modules (Image by ZHANG Jie)
＜関連情報＞
PestVL-Net：きめ細かな視覚言語インタラクションによるマルチモーダル害虫学習の実現 PestVL-Net: Enabling Multimodal Pest Learning via Fine-grained Vision-Language Interaction
Xueheng Li, Tao Hu, Ke Cao, Runsheng Qi, Huixin Zhang, Rui Li, Jie Zhang, Chengjun Xie
arXiv Submitted on 19 Apr 2026
DOI:https://doi.org/10.48550/arXiv.2604.17278
Abstract
Effective pest recognition and management are crucial for sustainable agricultural development. However, collecting pest data in real scenarios is often challenging. Compared to other domains, pests exhibit a wide variety of species with complex and diverse morphological characteristics. Existing techniques struggle to effectively model the key visual and high-level semantic features of pests in a fine-grained manner. These limitations hinder the practical application of such methods in real agricultural scenarios. To address these critical challenges, we present a synergistic approach that integrates PestVL-Net, a novel vision-language framework, with two multi-species pest datasets to facilitate fine-grained pest learning. The visual pathway of PestVL-Net utilizes the Recurrent Weighted Key Value (RWKV) architecture, incorporating a saliency-guided adaptive window partitioning scheme to effectively model the fine-grained visual characteristics of pests. Concurrently, the linguistic component generates precise pest semantic descriptions by leveraging Multimodal Large Language Models (MLLMs) priors, critically informed by agricultural expert knowledge and structured via multimodal Chain-of-Thought (CoT) reasoning. The deep fusion of these complementary visual and textual representations enables fine-grained multimodal pest learning. Extensive experimental evaluations on multiple pest datasets validate the superior performance of PestVL-Net, highlighting its potential for effective real-world pest management.
