The Future of Human-AI Interaction: InternLM-XComposer2.5-OmniLive

The interaction of AI systems with their environment over extended periods, similar to human cognition, is a long-standing research goal. Advances in multimodal large language models (MLLMs) have significantly improved open-world understanding. However, the challenge of continuous and simultaneous streaming perception, storage, and reasoning remains largely unexplored.

Current MLLMs are limited by their sequence-to-sequence architecture, which restricts their ability to process input and generate responses concurrently. They cannot, so to speak, think while they perceive. Furthermore, relying on long contexts to store historical data is impractical for long-term interactions, as storing all information becomes costly and inefficient.

Instead of relying on a single base model to perform all functions, InternLM-XComposer2.5-OmniLive (IXC2.5-OL) draws inspiration from the concept of "Specialized Generalist AI" and introduces decoupled streaming perception, reasoning, and memory mechanisms, enabling real-time interaction with streaming video and audio input.

The Three Key Modules of IXC2.5-OL

The proposed IXC2.5-OL framework consists of three key modules:

1. Streaming Perception Module: Processes multimodal information in real-time, stores important details in memory, and triggers reasoning in response to user queries.

2. Multimodal Long-Term Memory Module: Integrates short-term and long-term memory, and compresses short-term memory content into long-term memory content for efficient retrieval and improved accuracy.

3. Reasoning Module: Answers queries and performs reasoning tasks, coordinating with the perception and memory modules.

This project simulates human cognition and allows multimodal large language models to offer continuous and adaptive services over time. By combining real-time perception, efficient memory management, and advanced reasoning, IXC2.5-OL enables more natural and effective interaction with AI systems.

Applications and Potential

The ability to process and interpret streaming video and audio in real-time opens up a wide range of application possibilities. From assisting with everyday tasks to complex interactions in professional environments, IXC2.5-OL lays the foundation for a new generation of AI assistants.

For Mindverse as a German provider of AI solutions, this technology offers exciting possibilities. Integrating IXC2.5-OL into existing and future products could significantly improve functionality and user-friendliness. For example, chatbots and voicebots could respond more contextually and helpfully by processing video and audio data in real-time. The technology also offers the potential for AI search engines and knowledge systems to extract and process information from multimedia sources more effectively.

The development of IXC2.5-OL is a significant step towards a future where AI systems are seamlessly integrated into our everyday lives and can support us in a variety of tasks.

Bibliography

Zhang, P., et al. "InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal System for Long-term Streaming Video and Audio Interactions." arXiv preprint arXiv:2407.03320 (2024).

InternLM/InternLM-XComposer. GitHub repository.

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Hugging Face. "internlm/internlm-xcomposer2d5-ol-7b."

Dong, X., et al. "InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model." arXiv preprint arXiv:2401.16420 (2024).

Show Lab. "VideoLLM-online."

Song, C. "Video question answering based on temporal logic." (2024).

Chen, J., et al. "VideoLLM-online: Online Video Large Language Model for Streaming Video."