arxiv_ai 85% Match Research Paper Computer Vision Researchers,ML Engineers,Developers of Transformer-based vision models 2 weeks ago

REOrdering Patches Improves Vision Models

computer-vision › scene-understanding

📄 Abstract

Abstract: Sequence models such as transformers require inputs to be represented as one-dimensional sequences. In vision, this typically involves flattening images using a fixed row-major (raster-scan) order. While full self-attention is permutation-equivariant, modern long-sequence transformers increasingly rely on architectural approximations that break this invariance and introduce sensitivity to patch ordering. We show that patch order significantly affects model performance in such settings, with simple alternatives like column-major or Hilbert curves yielding notable accuracy shifts. Motivated by this, we propose REOrder, a two-stage framework for discovering task-optimal patch orderings. First, we derive an information-theoretic prior by evaluating the compressibility of various patch sequences. Then, we learn a policy over permutations by optimizing a Plackett-Luce policy using REINFORCE. This approach enables efficient learning in a combinatorial permutation space. REOrder improves top-1 accuracy over row-major ordering on ImageNet-1K by up to 3.01% and Functional Map of the World by 13.35%.

Authors (5)

Declan Kutscher

David M. Chan

Yutong Bai

Trevor Darrell

Ritwik Gupta

Submitted

May 29, 2025

arXiv Category

cs.LG

arXiv PDF

Key Contributions

Proposes REOrder, a framework that learns task-optimal patch orderings for vision transformers. It first derives an information-theoretic prior and then uses a Plackett-Luce policy optimized with REINFORCE to efficiently search the combinatorial permutation space, significantly improving model performance by addressing sensitivity to fixed ordering.

Business Value

Enhances the performance of vision models, leading to more accurate and reliable applications in areas like autonomous driving, medical imaging analysis, and surveillance.

Paper Metadata

Innovation Type

Novel Framework/Methodology

Deployment Feasibility

Moderate. Requires an additional training stage to learn the optimal ordering, but the inference process itself is compatible with existing transformer architectures.

Limitations Addressed

Addresses the performance degradation in modern long-sequence transformers caused by their sensitivity to fixed patch ordering (e.g., raster-scan), which breaks permutation invariance.

Performance Gains

Notable accuracy shifts and improved model performance due to optimized patch ordering.

Technical Tags

vision transformerspatch orderingpermutation invariancereinforcement learningPlackett-Luce policyinformation theorycombinatorial optimizationsequence models

Research Topics

Vision TransformersInput RepresentationModel SensitivityLearning Optimal OrderingsComputer Vision

Methods & Architectures

REOrder frameworkInformation-theoretic priorPlackett-Luce policy learningREINFORCE algorithmSequence modeling Vision TransformersLong-sequence TransformersSequence Models

Applications & Tasks

Image Recognition Object Detection Image Segmentation Computer Vision Tasks Sensitivity of transformers to patch orderingLoss of permutation invariance in approximationsSuboptimal performance due to fixed orderingDifficulty in learning optimal orderings Improving vision model accuracyDiscovering task-optimal patch orderingsAdapting input sequences for transformers

Related Fields

Computer VisionDeep LearningTransformersReinforcement LearningSequence Modeling

Keywords

vision transformerspatch orderingsequence modelspermutationreinforcement learningPlackett-LuceREINFORCEinput representationaccuracyoptimizationcombinatorial

Academic Context

#Vision Transformers#Input Representation#Model Sensitivity#Learning Optimal Orderings#Computer Vision

Commercial Potential

Potential Products

Optimized vision model componentsPre-trained vision transformers with learned orderingsTools for input sequence optimization

Target Industries

TechnologyAutomotiveHealthcareSecurityRobotics

Use Case Examples

Improving object detection accuracy in autonomous vehiclesEnhancing image segmentation for medical diagnosticsBoosting performance of surveillance systems

Competitive Edge

Offers a novel method to optimize input representation for vision transformers, addressing a key limitation of current architectures and potentially outperforming models relying on fixed ordering.

Market Opportunity

Large, as vision transformers are widely used across many industries.

Revenue Models

Licensing of the REOrder frameworkConsulting services for vision model optimization

Resource Requirements

Compute Needs

Requires significant compute for training the RL policy over permutations, in addition to standard vision model training.

Data Requirements

Standard image datasets used for vision tasks (e.g., ImageNet).

Deployment Constraints

Additional training complexity,Computational cost of learning orderings

Scalability

The REINFORCE approach for learning permutations can be computationally intensive, potentially limiting scalability for extremely large models or datasets.

Production Readiness

Maturity Level

Research

Time to Market

1-2 years for integration into existing vision model pipelines.

Patent Potential

Moderate, related to the REOrder framework and the method of learning optimal patch orderings.

View Full Paper Back to Papers