LLaVA-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models

Feng Li, Renrui Zhang, Hao Zhang, Yuanhan Zhang, Bo Li, Wei Li, Zejun MA, Chunyuan Li

Keywords: large language model, multimodal learning, interleaved image-text

Abstract:

Visual instruction tuning has made considerable strides in enhancing the capabilities of Large Multimodal Models (LMMs). However, existing open LMMs largely focus on single-image tasks, their applications to multi-image scenarios remains less explored. Additionally, prior LMM research separately tackles different scenarios, leaving it impossible to generalize cross scenarios with new
emerging capabilities. To this end, we introduce LLaVA-Interleave, which simultaneously tackles Multi-image, Multi-frame (video), Multi-view (3D), and Multi-patch (single-image) scenarios in LMMs. To enable these capabilities, we regard the interleaved data format as a general template and compile the M4-Instruct dataset with 1,177.6k samples, spanning 4 primary domains with 14
tasks and 41 datasets. We also curate the LLaVA-Interleave Bench to comprehensively evaluate the multi-image performance of LMMs. Through extensive
experiments, LLaVA-Interleave achieves leading results in multi-image, video,
and 3D benchmarks, while maintaining the performance of single-image tasks.
Besides, our model also exhibits several emerging capabilities, e.g., transferring tasks across different settings and modalities.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 2791