Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

Multimodal Large Language Model (MLLM) recently has been a new rising research
hotspot, which uses powerful Large Language Models (LLMs) as a brain to perform …

Artificial intelligence (AI) has elicited much attention across disciplines and industries (Hyder
et al., 2019). AI has been defined as “a system's ability to correctly interpret external data, to …

Visual language models (VLMs) rapidly progressed with the recent success of large
language models. There have been growing efforts on visual instruction tuning to extend the …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

In the realm of large multi-modal models (LMMs), efficient modality alignment is crucial yet
often constrained by the scarcity of high-quality image-text data. To address this bottleneck …

Large language models have demonstrated impressive universal capabilities across a wide
range of open-ended tasks and have extended their utility to encompass multimodal …

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

Multimodal Large Language Models (MLLMs) that integrate text and other modalities
(especially vision) have achieved unprecedented performance in various multimodal tasks …

Connecting text and visual modalities plays an essential role in generative intelligence. For
this reason, inspired by the success of large language models, significant research efforts …