3 citations · 7 across the 12 of their papers we have counts for
13 papers
TryOnReward: Learning Foveated Consistency for Reinforcement Fine-Tuning of Virtual Try-On
Xueheng Li, Yong Liu, Xiaolong Fu +5
Virtual Try-On (VTON) aims to dress a person with the reference garment, producing visually reasonable results aligned with human preferences. Turning this preference-oriented goal…
VideoSearcher: Empowering Video Deep Research with Multi-Tool Agentic Reasoning via Reinforcement Learning
Zhenkun Gao, Yicheng Bao, Jinlong Peng +13
Video understanding is moving beyond closed-context perception toward open-world evidence exploration, a paradigm formalized as Video Deep Research (VDR). However, existing multimo…
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…
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…
When Classes Evolve: A Benchmark and Framework for Stage-Aware Class-Incremental Learning
Zheng Zhang, Tao Hu, Xueheng Li +4
Class-Incremental Learning (CIL) aims to sequentially learn new classes while mitigating catastrophic forgetting of previously learned knowledge. Conventional CIL approaches implic…
Q-Probe: Scaling Image Quality Assessment to High Resolution via Context-Aware Agentic Probing
Xiang Li, Xueheng Li, Yu Wang +4
Reinforcement Learning (RL) has empowered Multimodal Large Language Models (MLLMs) to achieve superior human preference alignment in Image Quality Assessment (IQA). However, existi…
O-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering
Jianbiao Mei, Tao Hu, Daocheng Fu +11
Large Language Models (LLMs), despite their advancements, are fundamentally limited by their static parametric knowledge, hindering performance on tasks requiring open-domain up-to…
Shuffle Mamba: State Space Models with Random Shuffle for Multi-Modal Image Fusion
Ke Cao, Xuanhua He, Tao Hu +3
Multi-modal image fusion integrates complementary information from different modalities to produce enhanced and informative images. Although State-Space Models, such as Mamba, are…
Training-Free Large Model Priors for Multiple-in-One Image Restoration
Xuanhua He, Lang Li, Yingying Wang +7
Image restoration aims to reconstruct the latent clear images from their degraded versions. Despite the notable achievement, existing methods predominantly focus on handling specif…
Pan-Mamba: Effective pan-sharpening with State Space Model
Xuanhua He, Ke Cao, Keyu Yan +4
Pan-sharpening involves integrating information from low-resolution multi-spectral and high-resolution panchromatic images to generate high-resolution multi-spectral counterparts.…
Frequency-Adaptive Pan-Sharpening with Mixture of Experts
Xuanhua He, Keyu Yan, Rui Li +3
Pan-sharpening involves reconstructing missing high-frequency information in multi-spectral images with low spatial resolution, using a higher-resolution panchromatic image as guid…