In the past year, MultiModal Large Language Models (MM-LLMs) have undergone
substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs …

Perceptual video quality assessment plays a vital role in the field of video processing due to
the existence of quality degradations introduced in various stages of video signal …

Modality alignment serves as the cornerstone for large multi-modal models (LMMs).
However, the impact of different attributes (eg, data type, quality, and scale) of training data …

Abstract Multi-modal Large Language Models (MLLMs) have demonstrated impressive
instruction abilities across various open-ended tasks. However previous methods have …

Abstract The Large Vision-Language Model (LVLM) field has seen significant
advancements, yet its progression has been hindered by challenges in comprehending fine …

We present LLaVA-OneVision, a family of open large multimodal models (LMMs) developed
by consolidating our insights into data, models, and visual representations in the LLaVA …

In this work, we introduce Mini-Gemini, a simple and effective framework enhancing multi-
modality Vision Language Models (VLMs). Despite the advancements in VLMs facilitating …

We introduce InternLM-XComposer2, a cutting-edge vision-language model excelling in free-
form text-image composition and comprehension. This model goes beyond conventional …

Large vision-language models (LVLMs) have recently achieved rapid progress, sparking
numerous studies to evaluate their multi-modal capabilities. However, we dig into current …

We propose InternLM-XComposer, a vision-language large model that enables advanced
image-text comprehension and composition. The innovative nature of our model is …