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 …

With the widespread application of the Transformer architecture in various modalities,
including vision, the technology of large language models is evolving from a single modality …

Large multimodal models (LMM) have recently shown encouraging progress with visual
instruction tuning. In this paper we present the first systematic study to investigate the design …

In this paper, we introduce InternVL 1.5, an open-source multimodal large language model
(MLLM) to bridge the capability gap between open-source and proprietary commercial …

In this work, we discuss building performant Multimodal Large Language Models (MLLMs).
In particular, we study the importance of various architecture components and data choices …

In this work, we present a novel method to tackle the token generation challenge in Vision
Language Models (VLMs) for video and image understanding, called LLaMA-VID. Current …

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

Abstract This paper presents LLaVA-Plus (L arge L anguage a nd V ision A ssistants that P
lug and L earn to U se S kills), a general-purpose multimodal assistant trained using an end …

Abstract Large Multimodal Models (LMMs) extend Large Language Models to the vision
domain. Initial LMMs used holistic images and text prompts to generate ungrounded textual …

The exponential growth of large language models (LLMs) has opened up numerous
possibilities for multi-modal AGI systems. However the progress in vision and vision …