AI Intern for Multimedia Analysis 🤓🎦
So essentially,
InternLM-XComposer-2.5, a 7B Vision Language Model, handles long format image and video processing
Paper:
InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output (18 Pages)
Github:
https://github.com/InternLM/InternLM-XComposer
Researchers from Shanghai Artificial Intelligence Laboratory, The Chinese University of Hong Kong, SenseTime Group and Tsinghua University are interested in large-vision language model that supports long input and output. They want vision language models to provide more comprehensive answers.
Hmm..What’s the background?
Recent developments in large language models (LLMs) have driven the evolution of LVLMs, with models such as GPT-4, Gemini Pro 1.5, and Claude 3 significantly broadening LLM applications. While open-source LVLMs are evolving rapidly, they are hindered by limitations in handling long-context input and output and a lack of training corpus diversity, making them less versatile than closed-source models.
The paper introduces InternLM-XComposer-2.5 (IXC-2.5), a large vision language model (LVLM) designed to handle lengthy inputs and outputs in various text-image comprehension and composition tasks.
Ok, So what is proposed in the research paper?
IXC-2.5 leverages a 7B LLM backend and is trained with 24K interleaved image-text contexts, extendable to 96K long contexts using RoPE extrapolation
IXC-2.5 over its 2.0 version includes ultra-high resolution understanding, fine-grained video understanding, and multi-turn multi-image dialogue.
IXC-2.5 utilizes extra LoRA parameters for text-image composition applications, such as webpage crafting and composing text-image articles.
IXC-2.5 outperforms existing open-source models on 16 out of 28 benchmarks, matching or surpassing GPT-4V and Gemini Pro on 16 key tasks.
Source: https://huggingface.co/papers/2407.03320
What’s next?
The authors propose that future work for the InternLM-XComposer-2.5 model could explore extending its capabilities to a more contextual multi-modal environment, including long-context video understanding (e.g., long movies) and long-context interaction history. The authors suggest that these advancements would allow the model to better assist humans in real-world applications.
So essentially,
InternLM-XComposer-2.5, a 7B Vision Language Model, handles long format image and video processing