6 citations · 15 across the 12 of their papers we have counts for
12 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…
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…
Distilling Textual Priors from LLM to Efficient Image Fusion
Ran Zhang, Xuanhua He, Ke Cao +4
Multi-modality image fusion aims to synthesize a single, comprehensive image from multiple source inputs. Traditional approaches, such as CNNs and GANs, offer efficiency but strugg…
RelaCtrl: Relevance-Guided Efficient Control for Diffusion Transformers
Ke Cao, Jing Wang, Ao Ma +11
The Diffusion Transformer plays a pivotal role in advancing text-to-image and text-to-video generation, owing primarily to its inherent scalability. However, existing controlled di…
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…
ID-Animator: Zero-Shot Identity-Preserving Human Video Generation
Xuanhua He, Quande Liu, Shengju Qian +5
Generating high-fidelity human video with specified identities has attracted significant attention in the content generation community. However, existing techniques struggle to str…
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.…
Enhancing RAW-to-sRGB with Decoupled Style Structure in Fourier Domain
Xuanhua He, Tao Hu, Guoli Wang +9
RAW to sRGB mapping, which aims to convert RAW images from smartphones into RGB form equivalent to that of Digital Single-Lens Reflex (DSLR) cameras, has become an important area o…
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…