5 papers
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…
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…
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…
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…
VL-GLUE: A Suite of Fundamental yet Challenging Visuo-Linguistic Reasoning Tasks
Shailaja Keyur Sampat, Mutsumi Nakamura, Shankar Kailas +4
Deriving inference from heterogeneous inputs (such as images, text, and audio) is an important skill for humans to perform day-to-day tasks. A similar ability is desirable for the…