VisionZip: Longer is Better but Not Necessary in Vision Language Models (2412.04467v1)

Abstract: Recent advancements in vision-LLMs have enhanced performance by increasing the length of visual tokens, making them much longer than text tokens and significantly raising computational costs. However, we observe that the visual tokens generated by popular vision encoders, such as CLIP and SigLIP, contain significant redundancy. To address this, we introduce VisionZip, a simple yet effective method that selects a set of informative tokens for input to the LLM, reducing visual token redundancy and improving efficiency while maintaining model performance. The proposed VisionZip can be widely applied to image and video understanding tasks and is well-suited for multi-turn dialogues in real-world scenarios, where previous methods tend to underperform. Experimental results show that VisionZip outperforms the previous state-of-the-art method by at least 5% performance gains across nearly all settings. Moreover, our method significantly enhances model inference speed, improving the prefilling time by 8x and enabling the LLaVA-Next 13B model to infer faster than the LLaVA-Next 7B model while achieving better results. Furthermore, we analyze the causes of this redundancy and encourage the community to focus on extracting better visual features rather than merely increasing token length. Our code is available at https://github.com/dvlab-research/VisionZip .

Analyzing Visual Token Redundancy with VisionZip in Vision-LLMs

The paper, "VisionZip: Longer is Better but Not Necessary in Vision LLMs," introduces an evaluation of visual token redundancy in popular vision-LLMs (VLMs). The research asserts that despite performance improvements through increased visual token lengths, excessive redundancy exists, leading to inefficiencies. The researchers propose VisionZip, a method to address this inefficiency, which reduces the number of visual tokens while maintaining model performance.

Key Methodology and Findings

The paper centers on the observation that popular vision encoders, such as CLIP and SigLIP, generate a significant amount of redundant visual tokens. This redundancy is primarily due to overlapping information within the visual tokens which do not substantially contribute to the overall performance of VLMs. The implication is that visual representations in these models are inefficiently encoded, consuming more computational resources than necessary.

The proposed VisionZip method offers a remedy by selecting informative tokens for input into the LLMs. A crucial insight driving this approach is the identification of 'dominant' tokens that aggregate the most information. Notably, VisionZip achieves this reduction without additional training, positioning it as a computationally efficient alternative to existing extensions of visual token length.

Significant Results

VisionZip is tested across multiple VLM architectures including LLaVA-1.5, LLaVA-NeXT, and Mini-Gemini, demonstrating competitive performance compared to state-of-the-art models, often surpassing them on efficiency metrics. The method illustrated a reduction in prefilling time by up to 8× and allowed larger models, such as LLaVA-NeXT 13B, to infer faster than their smaller counterparts while delivering superior results.

• The method outperformed recent approaches by at least 5% across various benchmarks.
• Achieved a performance retention above 90% while reducing visual token length by a significant margin.
• Enhanced computational efficiency, demonstrated by accelerated inference times and reduced GPU memory footprint in practical applications.

Implications and Future Outlook

VisionZip's findings encourage a shift in focus towards refining visual feature extraction processes rather than extending token length. The paper's suggestions are integral to optimizing the balance between performance and computational demand in VLMs, particularly in constrained environments like edge computing and robotics.

Moreover, the researchers indicate a potential new research direction concentrating on developing vision encoders capable of more robust and non-redundant feature extraction. As machine learning applications expand into ever-complex scenarios, such approaches will be vital to ensuring that efficiency gains do not come at the cost of accuracy or application breadth.

Conclusion

VisionZip illustrates an advanced understanding of token redundancy in VLMs. The method’s ability to maintain performance while significantly reducing computational load represents a crucial development for efficient AI model deployment. As an expert analysis, the research highlights both a methodological innovation and a strategic pivot toward improved model efficiency. Future investigations could explore integrating such token redundancy management in broader AI systems, fostering sustainable advancements in the capabilities of multimodal LLMs.