IF-Bench: Benchmarking and Enhancing MLLMs for Infrared Images with Generative Visual Prompting
This addresses a domain-specific gap in MLLM evaluation for infrared image understanding, though it is incremental as it builds on existing MLLM frameworks.
The authors tackled the lack of evaluation for multimodal large language models (MLLMs) on infrared images by introducing IF-Bench, a benchmark with 499 images and 680 question-answer pairs, and proposed a training-free method (GenViP) that improved performance across over 40 models.
Recent advances in multimodal large language models (MLLMs) have led to impressive progress across various benchmarks. However, their capability in understanding infrared images remains unexplored. To address this gap, we introduce IF-Bench, the first high-quality benchmark designed for evaluating multimodal understanding of infrared images. IF-Bench consists of 499 images sourced from 23 infrared datasets and 680 carefully curated visual question-answer pairs, covering 10 essential dimensions of image understanding. Based on this benchmark, we systematically evaluate over 40 open-source and closed-source MLLMs, employing cyclic evaluation, bilingual assessment, and hybrid judgment strategies to enhance the reliability of the results. Our analysis reveals how model scale, architecture, and inference paradigms affect infrared image comprehension, providing valuable insights for this area. Furthermore, we propose a training-free generative visual prompting (GenViP) method, which leverages advanced image editing models to translate infrared images into semantically and spatially aligned RGB counterparts, thereby mitigating domain distribution shifts. Extensive experiments demonstrate that our method consistently yields significant performance improvements across a wide range of MLLMs. The benchmark and code are available at https://github.com/casiatao/IF-Bench.