DyMU: Dynamic Merging and Virtual Unmerging for Efficient VLMs
We present DyMU, an efficient, training-free framework that dynamically
reduces the computational burden of vision-language models (VLMs) while
maintaining high task performance. Our approach comprises two key components.
First, Dynamic Token Merging (DToMe) reduces the number of visual token
embeddings by merging similar tokens based on image complexity, addressing the
inherent inefficiency of fixed-length outputs in vision transformers. Second,
Virtual Token Unmerging (VTU) simulates the expected token sequence for large
language models (LLMs) by efficiently reconstructing the attention dynamics of
a full sequence, thus preserving the downstream performance without additional
fine-tuning. Unlike previous approaches, our method dynamically adapts token
compression to the content of the image and operates completely training-free,
making it readily applicable to most state-of-the-art VLM architectures.
Extensive experiments on image and video understanding tasks demonstrate that
DyMU can reduce the average visual token count by 32%-85% while achieving
comparable performance to full-length models across diverse VLM architectures,
including the recently popularized AnyRes-based visual encoders. Furthermore,
through qualitative analyses, we demonstrate that DToMe effectively adapts
token reduction based on image complexity and, unlike existing systems,
provides users more control over computational costs. Project page:
https://mikewangwzhl.github.io/dymu/.
680af0c4175842e433ae348e
Zhenhailong Wang
680af0c4175842e433ae348f
Senthil Purushwalkam
680af0c4175842e433ae3490
Caiming Xiong
680af0c4175842e433ae3491
Silvio Savarese
680af0c4175842e433ae3492
Heng Ji
680af0c4175842e433ae3493
Ran Xu
claimed_verified
2025-05-15T10:33:19.970Z
6465c4c863e7e09dd02e3e1b
/avatars/200b029184d2616f98296a2c212f0785.svg
Ran Xu
user
xurantju
DyMU: Dynamic Merging and Virtual Unmerging for Efficient VLMs
We present DyMU, an efficient, training-free framework that dynamically
reduces the computational burden of vision-language models (VLMs) while
maintaining high task performance. Our approach comprises two key components.
First, Dynamic Token Merging (DToMe) reduces the number of visual token
embeddings by merging similar tokens based on image complexity, addressing the
inherent inefficiency of fixed-length outputs in vision transformers. Second,
Virtual Token Unmerging (VTU) simulates the expected token sequence for large
language models (LLMs) by efficiently reconstructing the attention dynamics of
a full sequence, thus preserving the downstream performance without additional
fine-tuning. Unlike previous approaches, our method dynamically adapts token
compression to the content of the image and operates completely training-free,
making it readily applicable to most state-of-the-art VLM architectures.
Extensive experiments on image and video understanding tasks demonstrate that
DyMU can reduce the average visual token count by 32%-85% while achieving
comparable performance to full-length models across diverse VLM architectures,
including the recently popularized AnyRes-based visual encoders. Furthermore,
through qualitative analyses, we demonstrate that DToMe effectively adapts
token reduction based on image complexity and, unlike existing systems,
provides users more control over computational costs. Project page:
https://mikewangwzhl.github.io/dymu/.
DyMU, an efficient, training-free framework, reduces the computational load of vision-language models by dynamically merging and unmerging tokens without performance loss.
Dynamic Token Merging DToMe Virtual Token Unmerging VTU vision-language models VLMs vision transformers large language models LLMs attention dynamics computational burden token compression AnyRes-based visual encoders