Systematic Review and Resource Compilation of Multimodal Large Language Models in Low-Level Vision

This GitHub resource compilation comprehensively sorts out the applications of multimodal large language models in low-level vision tasks, covering core technical directions such as visual encoder adaptation, language branch optimization, output head design, and parameter-efficient fine-tuning. It also organizes cutting-edge progress in extended application fields like medical image processing and remote sensing data handling, providing valuable references for researchers and developers.

The field of computer vision has long had a division between high-level vision (object detection, classification, etc.) and low-level vision (super-resolution, denoising, etc.). Traditional low-level vision relies on manual priors and deep learning models, while multimodal large models introduce natural language as a supervision signal and semantic guidance, bringing new solutions to low-level vision. This resource compilation systematically organizes the latest progress in this field.

The visual encoders of multimodal large models excel at extracting high-level semantics but struggle to capture low-level details. Researchers have proposed strategies like resolution scaling (supporting higher input resolution to preserve spatial details) and feature fusion (integrating features from different levels to balance semantic and detail perception), which have shown outstanding performance in image super-resolution and restoration tasks.

Low-level vision involves pixel-level operations, while language models process discrete symbols. The core challenge is cross-modal collaboration. Prompt learning discovers alignment methods through learnable prompt vectors; instruction fine-tuning guides the model to generate expected outputs via specific templates, such as using natural language instructions to guide image restoration.

Traditional multimodal models output discrete text tokens, but low-level vision requires continuous pixel values. The mainstream solution is the Tokenizer-decoder framework (encoding images into latent tokens and then reconstructing them into high-resolution images); some works explore combining diffusion models with language models to improve output quality.

Multimodal large models have large parameter scales, making full fine-tuning costly. Parameter-Efficient Fine-Tuning (PEFT) provides solutions: LoRA adapts via low-rank matrices; Adapter inserts lightweight modules into Transformer layers; freezing strategies selectively freeze some layers and only fine-tune relevant components to reduce computational overhead.

Low-level vision technology is widely applied in professional fields: in medical image processing, models can enhance CT/MRI images based on natural language descriptions to assist diagnosis; in remote sensing data processing, improving satellite image quality supports land monitoring and disaster assessment; it also shows prospects in CAD design, video processing, and other fields.

This resource compilation presents the technical landscape of multimodal large models reshaping low-level vision: visual encoders enhance detail perception, language branches achieve cross-modal alignment, output heads support pixel generation, PEFT lowers application thresholds, and expansions into professional fields prove practical value. It is a technical guide worth in-depth study.