Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding (2311.08046v3)

Abstract: LLMs have demonstrated impressive universal capabilities across a wide range of open-ended tasks and have extended their utility to encompass multimodal conversations. However, existing methods encounter challenges in effectively handling both image and video understanding, particularly with limited visual tokens. In this work, we introduce Chat-UniVi, a Unified Vision-LLM capable of comprehending and engaging in conversations involving images and videos through a unified visual representation. Specifically, we employ a set of dynamic visual tokens to uniformly represent images and videos. This representation framework empowers the model to efficiently utilize a limited number of visual tokens to simultaneously capture the spatial details necessary for images and the comprehensive temporal relationship required for videos. Moreover, we leverage a multi-scale representation, enabling the model to perceive both high-level semantic concepts and low-level visual details. Notably, Chat-UniVi is trained on a mixed dataset containing both images and videos, allowing direct application to tasks involving both mediums without requiring any modifications. Extensive experimental results demonstrate that Chat-UniVi consistently outperforms even existing methods exclusively designed for either images or videos. Code is available at https://github.com/PKU-YuanGroup/Chat-UniVi.

Analysis of "Chat-UniVi: Unified Visual Representation Empowers LLMs with Image and Video Understanding"

The paper "Chat-UniVi: Unified Visual Representation Empowers LLMs with Image and Video Understanding" introduces an approach to enhance LLMs by integrating image and video comprehension capabilities into a unified framework. This work addresses the challenges faced by current models in interpreting both images and videos, particularly focusing on the efficient use of visual tokens.

Methodology Overview

Chat-UniVi is designed to provide a cohesive understanding of multimodal inputs by representing images and videos through a set of dynamic visual tokens. The model leverages a uniform representation that accommodates spatial details for images and temporal relationships for videos. This approach efficiently utilizes a limited number of visual tokens, crucial for maintaining model performance.

Dynamic Visual Tokens

Dynamic visual tokens are central to this model. They allow the model to adaptively manage spatial and temporal information:

1. Spatial Visual Token Merging: Utilizing DPC-KNN, the model merges visual tokens with similar semantic meanings, allowing more significant focus on crucial image regions.
2. Temporal Visual Token Merging: The model segments videos into events and expands visual tokens across frames within these events. This segmentation ensures the capture of essential temporal information while minimizing token usage.
3. Multi-scale Representation: By employing a multi-step aggregation, the model provides multi-scale visual features, associating higher levels with abstract concepts and lower levels with visual details.

Numerical Results and Claims

The authors assert that Chat-UniVi consistently outpaces existing models focused solely on either images or videos. Notably, the model achieves this with fewer visual tokens, demonstrating efficiency and efficacy.

The paper reports impressive performance across several tasks:

• In image understanding, Chat-UniVi excels in conversation, detail description, and reasoning, achieving high scores in GPT-based evaluations.
• For video understanding, it surpasses state-of-the-art models dedicated to video tasks, indicating its robust handling of temporal data.
• The model also shows competitive performance in ScienceQA and zero-shot video QA tasks, highlighting its versatility.

Implications and Future Directions

The unified approach of Chat-UniVi suggests potential advancements in the development of more comprehensive LLMs that can seamlessly interpret multimodal data. By reducing reliance on separate encoders and dynamically adjusting visual token usage, the model offers a streamlined pathway towards expanding AI’s capabilities in understanding and generating contextually rich responses.

The paper’s insights could guide future developments in AI, especially in areas requiring simultaneous processing of varying input types, such as autonomous systems and interactive multimedia applications. Further exploration might include integrating additional modalities, such as audio, to broaden the model's reach.

Conclusion

Overall, Chat-UniVi represents a significant step in creating unified multimodal AI systems. Its innovative use of dynamic visual tokens and multi-scale representation integrates image and video comprehension into LLMs effectively and efficiently. While challenges such as hallucination and long-sequence processing remain, this paper lays foundational work for subsequent advancements in AI capabilities for rich multimodal interaction.