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 …

Vision systems that see and reason about the compositional nature of visual scenes are
fundamental to understanding our world. The complex relations between objects and their …

Large language models (LLMs) have revolutionized the field of artificial intelligence,
enabling natural language processing tasks that were previously thought to be exclusive to …

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 …

Natural language processing and 2D vision models have attained remarkable proficiency on
many tasks primarily by escalating the scale of training data. However, 3D vision tasks have …

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

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

We introduce Cambrian-1, a family of multimodal LLMs (MLLMs) designed with a vision-
centric approach. While stronger language models can enhance multimodal capabilities, the …

Large language models (LLMs) have been recently leveraged as training data generators
for various natural language processing (NLP) tasks. While previous research has explored …