Large language models (LLMs) have demonstrated extraordinary capabilities and
contributed to multiple fields, such as generating and summarizing text, language …

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 …

The emergence of large language models (LLMs) has marked a significant breakthrough in
natural language processing (NLP), fueling a paradigm shift in information acquisition …

While large language models (LLMs) have demonstrated remarkable capabilities across a
range of downstream tasks, a significant concern revolves around their propensity to exhibit …

Although perception systems have made remarkable advancements in recent years they still
rely on explicit human instruction or pre-defined categories to identify the target objects …

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 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 …

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 …

Humans can easily solve multimodal tasks in context with only a few demonstrations or
simple instructions which current multimodal systems largely struggle to imitate. In this work …