6 papers · 1 filter
Pest-Thinker: Learning to Think and Reason like Entomologists via Reinforcement Learning
Xueheng Li, Yu Wang, Tao Hu +6
Pest-induced crop losses pose a major threat to global food security and sustainable agricultural development. While recent advances in Multimodal Large Language Models (MLLMs) hav…
PestVL-Net: Enabling Multimodal Pest Learning via Fine-grained Vision-Language Interaction
Xueheng Li, Tao Hu, Ke Cao +5
Effective pest recognition and management are crucial for sustainable agricultural development. However, collecting pest data in real scenarios is often challenging. Compared to ot…
Multigrain-aware Semantic Prototype Scanning and Tri-Token Prompt Learning Embraced High-Order RWKV for Pan-Sharpening
Junfeng Li, Wenyang Zhou, Xueheng Li +3
In this work, we propose a Multigrain-aware Semantic Prototype Scanning paradigm for pan-sharpening, built upon a high-order RWKV architecture and a tri-token prompting mechanism d…
Cross-Scale Pansharpening via ScaleFormer and the PanScale Benchmark
Ke Cao, Xuanhua He, Xueheng Li +7
Pansharpening aims to generate high-resolution multi-spectral images by fusing the spatial detail of panchromatic images with the spectral richness of low-resolution MS data. Howev…
LookWise: Knowing When and Where to Look for Fine-Grained Visual Reasoning in Multimodal Large Language Models
Yuxiang Shen, Hailong Huang, Zhenkun Gao +6
Multimodal Large Language Models (MLLMs) are shifting towards "Thinking with Images" by actively exploring image details. While effective, large-scale training is computationally e…
Rethinking Pan-sharpening: A New Training Process for Full-Resolution Generalization
Ran Zhang, Xuanhua He, Li Xueheng +6
The field of pan-sharpening has recently seen a trend towards increasingly large and complex models, often trained on single, specific satellite datasets. This one-dataset, one-mod…