  Large Multimodal Models (LMMs) such as LLaVA have shown strong performance in
visual-linguistic reasoning. These models first embed images into a fixed large
number of visual tokens and then feed them into a Large Language Model (LLM).
However, this design causes an excessive number of tokens for dense visual
scenarios such as high-resolution images and videos, leading to great
inefficiency. While token pruning/merging methods do exist, they produce a
single length output for each image and do not afford flexibility in trading
off information density v.s. efficiency. Inspired by the concept of Matryoshka
Dolls, we propose M3: Matryoshka Multimodal Models, which learns to represent
visual content as nested sets of visual tokens that capture information across
multiple coarse-to-fine granularities. Our approach offers several unique
benefits for LMMs: (1) One can explicitly control the visual granularity per
test instance during inference, e.g. , adjusting the number of tokens used to
represent an image based on the anticipated complexity or simplicity of the
content; (2) M3 provides a framework for analyzing the granularity needed for
existing datasets, where we find that COCO-style benchmarks only need around ~9
visual tokens to obtain accuracy similar to that of using all 576 tokens; (3)
Our approach provides a foundation to explore the best trade-off between
performance and visual token length at sample level, where our investigation
reveals that a large gap exists between the oracle upper bound and current
fixed-scale representations.
