Fewer Tokens, Greater Scaling: Self-Adaptive Visual Bases for Efficient and Expansive Representation Learning
Proposes self-adaptive visual bases using Orthogonal Filtering to reduce visual tokens for efficient representation learning.
Key Findings
Methodology
This paper introduces an Orthogonal Filtering module based on the Minimum Description Length theory to adaptively cluster and reduce redundant visual tokens. The module consists of an allocator and multiple slots, where the allocator assigns image tokens to unique slots, and each slot fuses assigned tokens to form basis vectors. The allocator is trained with an orthogonality loss to ensure semantic independence among slots.
Key Results
- On the ImageNet dataset, the ViT-Huge model requires only 61 basis vectors to reach its performance upper bound, significantly reducing computational costs.
- Larger models maintain high generalization ability even with fewer tokens, confirming the complementary relationship between model capacity and token budget.
- A visual long-context dataset was constructed to further validate the potential of visual models in long-context understanding.
Significance
This study significantly reduces the training and inference costs of large-scale vision models by minimizing visual token redundancy, providing a new theoretical foundation for the scalability of Vision Transformers. By introducing the Orthogonal Filtering module, models maintain semantic completeness while improving computational efficiency, advancing research in scalable visual intelligence.
Technical Contribution
The paper introduces the Orthogonal Filtering module in visual representation learning, offering new theoretical guarantees and engineering possibilities compared to existing SOTA methods. It systematically analyzes the relationship between model capacity and visible token ratio through the Minimum Description Length theory, revealing the boundary of visual tokens sustaining complete semantics.
Novelty
This is the first application of the Minimum Description Length theory to compress visual tokens, proposing the Orthogonal Filtering module to reduce redundancy. Compared to existing methods, this approach maintains model performance and generalization ability while reducing the number of tokens.
Limitations
- The Orthogonal Filtering module may not effectively reduce token redundancy in certain complex scenarios, affecting model performance.
- The adaptability of this method to different model architectures needs further validation.
Future Work
Future research could explore the application of the Orthogonal Filtering module in other visual tasks and how to further optimize the module to adapt to different model architectures.
AI Executive Summary
Vision Transformers face challenges of redundant tokens when processing large-scale image data, limiting scalability and efficiency. Existing methods often rely on heuristic importance measures, failing to fully consider the intrinsic semantic structure of visual data.
This paper proposes an Orthogonal Filtering module based on the Minimum Description Length theory to adaptively cluster and reduce redundant visual tokens. The module consists of an allocator and multiple slots, where the allocator assigns image tokens to unique slots, and each slot fuses assigned tokens to form basis vectors. The allocator is trained with an orthogonality loss to ensure semantic independence among slots.
Experimental results show that larger models maintain high generalization ability even with fewer tokens, confirming the complementary relationship between model capacity and token budget. Additionally, a visual long-context dataset was constructed to further validate the potential of visual models in long-context understanding.
Deep Analysis
Background
Vision Transformers have made significant progress in image processing, but their scalability is limited by redundant visual tokens. Traditional methods often reduce redundancy through pruning or clustering but rely on specific model feature spaces, failing to fully consider the intrinsic semantic structure of visual data. The Minimum Description Length theory provides a new perspective for quantifying semantic redundancy among visual tokens.
Core Problem
Vision models face the challenge of redundant tokens when processing large-scale image data, limiting scalability and efficiency. Existing methods often rely on heuristic importance measures, failing to fully consider the intrinsic semantic structure of visual data.
Innovation
This paper proposes an Orthogonal Filtering module based on the Minimum Description Length theory to adaptively cluster and reduce redundant visual tokens. The module consists of an allocator and multiple slots, where the allocator assigns image tokens to unique slots, and each slot fuses assigned tokens to form basis vectors. The allocator is trained with an orthogonality loss to ensure semantic independence among slots.
Methodology
- �� Propose an Orthogonal Filtering module to adaptively cluster and reduce redundant visual tokens.
- �� The module consists of an allocator and multiple slots, where the allocator assigns image tokens to unique slots.
- �� Each slot fuses assigned tokens to form basis vectors, trained with an orthogonality loss to ensure semantic independence among slots.
Experiments
Experiments on the ImageNet dataset validate the impact of the Orthogonal Filtering module on model performance with reduced tokens. Results show that larger models maintain high generalization ability even with fewer tokens, confirming the complementary relationship between model capacity and token budget.
Results
Results show that the ViT-Huge model requires only 61 basis vectors to reach its performance upper bound, significantly reducing computational costs. Additionally, larger models maintain high generalization ability even with fewer tokens, confirming the complementary relationship between model capacity and token budget.
Applications
The Orthogonal Filtering module can reduce computational costs and improve scalability in vision models. It has broad applications in large-scale image processing, autonomous driving, and intelligent surveillance.
Limitations & Outlook
The Orthogonal Filtering module may not effectively reduce token redundancy in certain complex scenarios, affecting model performance. Additionally, the adaptability of this method to different model architectures needs further validation.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen with many ingredients, but you only need a few to make a delicious dish. The Orthogonal Filtering module acts like a smart chef who quickly picks out the most important ingredients, saving time and effort while still making a tasty meal.
ELI14 Explained like you're 14
Imagine you're playing a game where you need to collect important items to defeat enemies. But there are too many items, and you can't collect them all. The Orthogonal Filtering module is like a smart helper that picks out the most important items for you, so you can defeat enemies faster and save time and energy.
Glossary
Minimum Description Length
An information theory concept used to quantify data complexity.
Used to quantify semantic redundancy among visual tokens.
Orthogonal Filtering
A module that adaptively clusters and reduces redundant visual tokens.
Used to reduce visual token redundancy.
Vision Transformer
A visual model architecture based on Transformers.
Used for processing large-scale image data.
Visual Semantic Space
The semantic representation space of image tokens.
Used to represent the intrinsic semantic structure of images.
Allocator
A component of the Orthogonal Filtering module that assigns image tokens to unique slots.
Used in the Orthogonal Filtering module.
Open Questions Unanswered questions from this research
- 1 How to apply the Orthogonal Filtering module in different visual tasks?
- 2 How adaptable is the Orthogonal Filtering module to different model architectures?
Applications
Immediate Applications
Image Processing
Reduce token redundancy to improve efficiency and scalability in image processing.
Long-term Vision
Intelligent Surveillance
Reduce computational costs to improve real-time accuracy and efficiency in surveillance systems.
Abstract
This paper investigates the fundamental relationship between model capacity and the minimal number of visual tokens required to preserve image semantics. Inspired by the Minimum Description Length principle, we reinterpret image tokens as vectors in a visual semantic space and define the intrinsic semantic complexity of an image as the smallest set of basis vectors needed to span this space. Building on this perspective, we propose Orthogonal Filtering, a lightweight module that adaptively clusters redundant tokens into a compact set of orthogonal bases. Through extensive experiments across a range of ViT models, we reveal a consistent token, model scaling law: larger models require significantly fewer tokens to span visual semantic space. Besides, we also contribute a visual long-context dataset.