Large multimodal models trained on natural documents, which interleave images and text,
outperform models trained on image-text pairs on various multimodal benchmarks …

Multimodal datasets are a critical component in recent breakthroughs such as CLIP, Stable
Diffusion and GPT-4, yet their design does not receive the same research attention as model …

We present DeepSeek-VL, an open-source Vision-Language (VL) Model designed for real-
world vision and language understanding applications. Our approach is structured around …

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

Massive web datasets play a key role in the success of large vision-language models like
CLIP and Flamingo. However, the raw web data is noisy, and existing filtering methods to …

Due to the lack of paired samples and the low signal-to-noise ratio of functional MRI (fMRI)
signals, reconstructing perceived natural images or decoding their semantic contents from …

Large Language Models (LLMs) represent a class of deep learning models adept at
understanding natural language and generating coherent responses to various prompts or …

We introduce OmChat, a model designed to excel in handling long contexts and video
understanding tasks. OmChat's new architecture standardizes how different visual inputs are …

In recent years, significant advancements in large language models and visual foundation
models in the field of artificial intelligence have attracted scholars' attention to the potential of …

Despite the rapid development of Chinese vision-language models (VLMs), most existing
Chinese vision-language (VL) datasets are constructed on Western-centric images from …