VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding (2501.13106v2)

Abstract: In this paper, we propose VideoLLaMA3, a more advanced multimodal foundation model for image and video understanding. The core design philosophy of VideoLLaMA3 is vision-centric. The meaning of "vision-centric" is two-fold: the vision-centric training paradigm and vision-centric framework design. The key insight of our vision-centric training paradigm is that high-quality image-text data is crucial for both image and video understanding. Instead of preparing massive video-text datasets, we focus on constructing large-scale and high-quality image-text datasets. VideoLLaMA3 has four training stages: 1) Vision Encoder Adaptation, which enables vision encoder to accept images of variable resolutions as input; 2) Vision-Language Alignment, which jointly tunes the vision encoder, projector, and LLM with large-scale image-text data covering multiple types (including scene images, documents, charts) as well as text-only data. 3) Multi-task Fine-tuning, which incorporates image-text SFT data for downstream tasks and video-text data to establish a foundation for video understanding. 4) Video-centric Fine-tuning, which further improves the model's capability in video understanding. As for the framework design, to better capture fine-grained details in images, the pretrained vision encoder is adapted to encode images of varying sizes into vision tokens with corresponding numbers, rather than a fixed number of tokens. For video inputs, we reduce the number of vision tokens according to their similarity so that the representation of videos will be more precise and compact. Benefit from vision-centric designs, VideoLLaMA3 achieves compelling performances in both image and video understanding benchmarks.

Overview of VideoLLaMA3: Frontier Multimodal Foundation Models

The paper under discussion introduces VideoLLaMA3, an advanced multimodal foundation model designed to improve image and video understanding. The authors articulate a vision-centric approach that emphasizes high-quality image-text data as crucial to both image and video comprehension, shifting away from the conventional reliance on extensive video-text datasets. VideoLLaMA3 is structured around a vision-centric training paradigm with four distinct stages that progressively enhance the model's capability to interpret visual data.

The stages of training are as follows: firstly, the vision-centric alignment to warm up the vision encoder and projector; secondly, vision-language pretraining to jointly tune components using large-scale and diverse image-text datasets; thirdly, multi-task fine-tuning, which incorporates image-text data for downstream tasks as well as video-text data; finally, video-centric fine-tuning to further refine video understanding abilities.

The framework of VideoLLaMA3 is noteworthy for its focus on capturing fine-grained details. The authors adapt a pretrained vision encoder to accommodate images of varying sizes, thus creating scalable vision tokens relevant to their context. For video inputs, the model strategically reduces the number of vision tokens based on similarity measures to ensure precision and compactness, while maintaining computational efficiency.

The model's architecture reflects substantial improvements in benchmarks for both image and video understanding. The experiments compare VideoLLaMA3 to preceding models, where it achieves superior results in evaluations like VideoMME, PerceptionTest, MLVU for video understanding, and DocVQA and MathVista for image comprehension tasks. These results emphasize the model's performance in tasks that require detailed comprehension of static and dynamic visual data, illustrating its effectiveness across a range of applications.

Implications for Future Developments in AI

The research presented in this paper has significant implications for the development of AI systems capable of more nuanced understanding across multiple modalities. The emphasis on high-quality image-text data as a backbone for video comprehension highlights a practical approach toward addressing the complexities inherent in video data.

Practically, the insights from VideoLLaMA3 suggest that future models could benefit from a similar vision-centric approach, where sophisticated image understanding serves as the foundation for robust video analysis capabilities. The efficient repurposing of image-focused datasets could also streamline the development pipeline, reducing reliance on labor-intensive video data curation.

Theoretically, this work underscores the potential for transferring knowledge across modalities within AI systems, providing a roadmap for integrating various visual inputs within a unified architecture. The work invites further exploration into how vision-centric training paradigms can be adapted for other complex data types or combined with additional sensory inputs like audio, enhancing the AI's overall perceptual and reasoning capabilities.

In summary, VideoLLaMA3 represents a significant stride in the field of multimodal AI, reflecting a strategically sound methodology for simultaneous optimization of image and video understanding capabilities that sets a precedent for future research and development in the field.