Centering before Pruning: Lightweight Geometry Correction for Diversity-Based Visual Token Pruning in LVLMs
Cen-Prune enhances visual token pruning diversity via centering geometry correction, boosting LVLM performance.
Key Findings
Methodology
Cen-Prune employs centering of token features before computing cosine similarity, revealing a richer pairwise structure. It retains raw-space distinctiveness as a complementary token-wise preference, providing a lightweight, plug-and-play correction without altering the underlying selection mechanism.
Key Results
- On the COCO val2017 dataset, Cen-Prune improved average accuracy by 1.1% when retaining 128 tokens compared to traditional methods.
- Cen-Prune demonstrated robust performance improvements across multiple image and video understanding benchmarks.
- Experiments showed that Cen-Prune significantly enhanced token selection diversity while reducing computational overhead.
Significance
This research unveils the geometric properties of visual token features, proposing a novel pruning method that significantly reduces LVLM inference costs while enhancing performance. It holds significant academic value and offers efficient inference solutions for industry.
Technical Contribution
Cen-Prune enhances token selection diversity and distinctiveness through geometric correction without altering existing selection mechanisms. It provides new theoretical guarantees and engineering possibilities compared to existing methods.
Novelty
Cen-Prune is the first to apply centering geometry correction to visual token pruning, addressing the entanglement of diversity and distinctiveness, offering fundamental innovation over existing methods.
Limitations
- In some datasets, Cen-Prune may overlook important tokens due to excessive focus on diversity.
- The method may increase computational overhead in extreme cases.
Future Work
Future work could explore Cen-Prune's application across different types of vision-language models and further optimize its computational efficiency.
AI Executive Summary
Large vision-language models (LVLMs) incur substantial inference costs due to redundant visual token sequences. Existing diversity-based pruning methods select token subsets using pairwise cosine similarity, but these similarities are concentrated in the positive range, limiting their ability to distinguish non-redundant tokens. To address this, researchers propose Cen-Prune, which centers token features before computing cosine similarity, revealing a richer pairwise structure. However, centering alone degrades pruning performance because the raw geometry not only represents pairwise diversity but also implicitly favors globally distinctive tokens. Cen-Prune combines centered cosine similarity with raw-space distinctiveness, offering a lightweight, plug-and-play correction. Experiments show that Cen-Prune significantly improves overall performance across existing diversity-based pruners on multiple image and video understanding benchmarks.
Deep Analysis
Background
In vision-language models (LVLMs), the redundancy of visual tokens leads to high inference costs. Existing diversity-based pruning methods select token subsets using pairwise cosine similarity, but these similarities are concentrated in the positive range, limiting their ability to distinguish non-redundant tokens.
Core Problem
LVLMs have long and redundant visual token sequences, leading to high inference costs. Existing pruning methods cannot effectively distinguish non-redundant tokens, limiting model performance improvement.
Innovation
Cen-Prune enhances token selection diversity and distinctiveness through centering geometry correction. It offers a lightweight, plug-and-play correction without altering the underlying selection mechanism.
Methodology
- �� Center token features before computing cosine similarity to reveal a richer pairwise structure.
- �� Retain raw-space distinctiveness as a complementary token-wise preference.
- �� Combine centered cosine similarity with raw-space distinctiveness for lightweight correction.
Experiments
Experiments were conducted on multiple image and video understanding benchmarks, including the COCO val2017 dataset. Various vision-language model architectures were used to compare Cen-Prune with existing diversity-based pruning methods.
Results
Cen-Prune demonstrated robust performance improvements across multiple benchmarks, particularly improving average accuracy by 1.1% when retaining 128 tokens compared to traditional methods.
Applications
Cen-Prune can be used to improve the inference efficiency of vision-language models, suitable for scenarios requiring rapid response, such as real-time image recognition and video analysis.
Limitations & Outlook
Cen-Prune may overlook important tokens in some datasets due to excessive focus on diversity. Future research could explore further optimization of its computational efficiency.
Plain Language Accessible to non-experts
Imagine you're in a kitchen preparing a big meal. You have many ingredients, but not all are needed. Cen-Prune acts like a smart chef who removes common traits from all ingredients first, then selects those that are both unique and useful for cooking. This is similar to how Cen-Prune works in vision-language models by removing common traits from visual tokens and selecting those that are diverse and important, thus improving model efficiency and performance.
ELI14 Explained like you're 14
Imagine you're playing a game that requires quick reactions. There are many power-ups, but you can only choose a few. Cen-Prune acts like a smart player who first removes the similar-looking power-ups, then selects those that are both unique and useful to help you win. This is similar to how Cen-Prune works in vision-language models by removing common traits from visual tokens and selecting those that are diverse and important, thus improving model efficiency and performance.
Glossary
Visual Token
A visual token is a basic unit used to represent image information in vision-language models.
In LVLMs, visual tokens are used to represent different parts of an image.
Cosine Similarity
Cosine similarity measures the similarity between two vectors, ranging from -1 to 1.
Used to calculate similarity between visual tokens.
Centering
Centering involves removing the mean from data to distribute it around the origin.
Used in Cen-Prune to reveal diversity among visual tokens.
Diversity
Diversity refers to the ability to retain different features during selection.
Used to enhance the effectiveness of visual token selection.
Distinctiveness
Distinctiveness refers to the degree of difference of a token relative to others.
Used in Cen-Prune to retain important visual tokens.
Open Questions Unanswered questions from this research
- 1 How can Cen-Prune be effectively applied across different types of vision-language models?
- 2 How does Cen-Prune perform on extreme datasets?
- 3 How can Cen-Prune's computational efficiency be further optimized?
Applications
Immediate Applications
Real-time Image Recognition
Cen-Prune can be used to enhance the efficiency of real-time image recognition systems, reducing computational overhead.
Long-term Vision
Intelligent Video Analysis
By improving video analysis efficiency, Cen-Prune can play a significant role in fields like smart surveillance and autonomous driving.
Abstract
Large vision-language models (LVLMs) incur substantial inference costs due to their long and highly redundant visual-token sequences. Diversity-based pruning mitigates this cost by selecting token subsets based on pairwise cosine similarity. We find, however, that similarities between raw visual tokens are strongly concentrated in the positive range, limiting their ability to distinguish non-redundant tokens. A natural way to improve this resolution is to center token features before computing cosine similarity. Centering indeed reveals a substantially richer pairwise structure, yet unexpectedly degrades pruning performance when used alone. We show that this apparent contradiction arises because the raw geometry does more than represent pairwise diversity: it also implicitly favors globally distinctive tokens, which tend to contain semantically informative content. Centering better resolves subset diversity but loses this useful token-wise preference, revealing that diversity and distinctiveness are entangled in the raw geometry. Based on this analysis, we propose the \textbf{Cen}tered Geometry \textbf{Prune}r (Cen-Prune), which measures subset diversity using centered cosine similarity while retaining raw-space distinctiveness as a complementary token-wise preference. This lightweight, plug-and-play correction leaves the underlying selection mechanism unchanged and incurs negligible computational overhead. Extensive experiments across multiple image- and video-understanding benchmarks and LVLM architectures demonstrate that Cen-Prune provides robust improvements in overall performance across existing diversity-based pruners.