Large Multi Modality Model

Large multi-modality models (LMMs) integrate diverse data types, such as images and text, to perform complex tasks exceeding the capabilities of unimodal models. Current research emphasizes improving LMM performance in visual quality assessment, geolocalization, and visual grounding, often employing retrieval-augmented generation and preference-based reinforcement learning techniques. These advancements are driving progress in various fields, including image analysis, information retrieval, and AI-generated content evaluation, by enabling more nuanced and accurate understanding of multimodal data.