Keywords
token compression
Abstract
Vision-language evidence modeling requires a balance between semantic richness and computational efficiency. This topic connects forensic vision-language compression with biomedical and remote sensing image evidence. Token-efficient representation is important for forensic analysis, where excessive visual tokens may increase cost without proportionally improving interpretability. Instance-aware super-resolution and hyperspectral-LiDAR fusion further enhance visual evidence quality across complex scenes. In biomedical contexts, DCE-MRI breast cancer segmentation, low-dose medical imaging, melanoma detection, immune microenvironment analysis, exosome-based prognosis, and vaccine side-effect monitoring provide high-stakes examples where image evidence must be both efficient and reliable. The literature structure supports a cross-domain model of visual evidence processing that integrates token compression, image restoration, multimodal fusion, and clinical interpretability.
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