Summary of Internlm-xcomposer2.5-omnilive: a Comprehensive Multimodal System For Long-term Streaming Video and Audio Interactions, by Pan Zhang et al.

InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal System for Long-term Streaming Video and Audio Interactions

by Pan Zhang, Xiaoyi Dong, Yuhang Cao, Yuhang Zang, Rui Qian, Xilin Wei, Lin Chen, Yifei Li, Junbo Niu, Shuangrui Ding, Qipeng Guo, Haodong Duan, Xin Chen, Han Lv, Zheng Nie, Min Zhang, Bin Wang, Wenwei Zhang, Xinyue Zhang, Jiaye Ge, Wei Li, Jingwen Li, Zhongying Tu, Conghui He, Xingcheng Zhang, Kai Chen, Yu Qiao, Dahua Lin, Jiaqi Wang

First submitted to arxiv on: 12 Dec 2024

GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The proposed framework, InternLM-XComposer2.5-OmniLive (IXC2.5-OL), aims to create AI systems that can interact with environments like humans do. Building on recent advancements in multimodal large language models (MLLMs), this project addresses the challenge of simultaneous and continuous processing of perception, memory, and reasoning. The framework consists of three modules: Streaming Perception Module, Multi-modal Long Memory Module, and Reasoning Module. It simulates human-like cognition by processing multimodal information in real-time, storing key details in memory, and triggering reasoning in response to user queries.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This project aims to create AI systems that can interact with environments like humans do. The goal is to make AI systems that can understand and respond to information from multiple sources over a long period of time. Right now, most AI systems are not very good at this because they process information one step at a time, rather than all at once. To fix this, the researchers propose a new framework called InternLM-XComposer2.5-OmniLive (IXC2.5-OL) that has three main parts: one for perceiving and processing information in real-time, another for storing and retrieving memories, and a third for reasoning and making decisions.