LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models
In July 2024, the LLaVA team released LLaVA-NeXT-Interleave, which unifies multi-image, multi-frame (video), multi-view (3D), and multi-patch (single image) scenarios through an interleaved data format. They constructed the M4-Instruct dataset with 1,177,600 samples, covering 4 major domains, 14 tasks, and 41 datasets. The model achieves leading results on multi-image, video, and 3D benchmarks while maintaining single-image task performance.
This work addresses the shortcomings of open multimodal large models in multi-image scenarios, enabling a single model to handle various visual input forms through a unified data format and training strategy. This reduces the complexity of multimodal AI systems and lays the foundation for building general-purpose visual assistants.
It adopts an interleaved data format as a universal template to uniformly represent multi-images, video frames, 3D views, and single-image patches. The M4-Instruct dataset is constructed with 1,177,600 samples covering 14 tasks. The model is based on the LLaVA-NeXT architecture and achieves multi-task learning through instruction fine-tuning. Experiments show that the model reaches leading performance on multi-image, video, and 3D benchmarks without sacrificing single-image performance.
This model provides a unified solution for multimodal AI applications (e.g., video understanding, 3D scene analysis, multi-document QA), potentially reducing development costs and improving user experience. It holds significant reference value for industries that need to process multiple visual inputs (e.g., autonomous driving, robotics, medical imaging).
It is recommended that multimodal AI product teams evaluate the applicability of LLaVA-NeXT-Interleave in scenarios such as video analysis, multi-image comparison, and 3D scene understanding. Domain-specific fine-tuning can be performed based on the M4-Instruct dataset to quickly build vertical applications.
Focus on the model's scalability with larger data and more complex tasks, as well as integration with other modalities (e.g., audio, touch). More multimodal foundation models based on unified formats are expected to emerge.