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April 26, 2025
Visual Language Models (VLMs) are gaining increasing importance in the world of Artificial Intelligence. They enable the processing and understanding of both text and image information, leading to innovative applications in areas such as image captioning, question answering, and visual search. However, the complexity of these models poses a challenge, particularly in terms of computational power and memory requirements. A promising approach to addressing this challenge is DyMU, a method of dynamic merging and virtual unmerging of tokens.
VLMs are based on the processing of tokens, which represent both text fragments and visual information. The simultaneous processing of these different data types requires significant computational resources. The larger and more complex the models become, the higher the demand for computing power and memory. This limits the application possibilities of VLMs, especially on devices with limited resources.
DyMU (Dynamic Merging and Virtual Unmerging) offers an elegant solution to this problem. The method is based on the idea of dynamically merging and virtually unmerging tokens to reduce computational complexity without significantly impacting model accuracy. At its core, DyMU combines tokens that carry similar information into a single token. This process reduces the number of tokens to be processed and thus the computational effort. At the same time, through virtual unmerging, the model retains the ability to access the original information when required for the specific task.
DyMU uses a sophisticated system to decide which tokens can be merged. It uses similarity metrics to identify tokens that are redundant or very similar. The merged tokens then represent the combined information of the original tokens. If necessary, these can be virtually separated back into their original components to retrieve more detailed information. This dynamic process enables efficient processing without reducing the information density of the model.
By dynamically adjusting the number of tokens, DyMU offers several advantages:
Increased Computing Power: Reducing the number of tokens leads to a significant acceleration of processing and enables the use of VLMs on less powerful devices.
Lower Memory Requirements: The more efficient use of resources reduces memory requirements, which in turn improves the scalability of VLMs.
Improved Accuracy: By focusing on relevant information, DyMU can even improve the accuracy of VLMs in some cases.
DyMU is a promising approach to optimizing VLMs. The dynamic adjustment of the number of tokens enables efficient and scalable processing of multimodal data. Future research could focus on further developing the algorithms for token merging and unmerging to further improve the efficiency and accuracy of VLMs. This could pave the way for new applications and broader use of VLMs in various fields.
Bibliographie: - https://www.arxiv.org/abs/2504.17040 - https://mikewangwzhl.github.io/dymu/ - https://twitter.com/_akhaliq/status/1915672837425074521 - https://paperreading.club/page?id=301571 - https://x.com/_akhaliq/status/1915672839933378905 - https://github.com/daixiangzi/Awesome-Token-Compress - https://huggingface.co/papers - https://mikewangwzhl.github.io/ - https://www.chatpaper.ai/papers