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DRIP: Dynamic patch Reduction via Interpretable Pooling

computer-vision › object-detection

📄 Abstract

Abstract: Recently, the advances in vision-language models, including contrastive pretraining and instruction tuning, have greatly pushed the frontier of multimodal AI. However, owing to the large-scale and hence expensive pretraining, the efficiency concern has discouraged researchers from attempting to pretrain a vision language model from scratch. In this work, we propose Dynamic patch Reduction via Interpretable Pooling (DRIP), which adapts to the input images and dynamically merges tokens in the deeper layers of a visual encoder. Our results on both ImageNet training from scratch and CLIP contrastive pretraining demonstrate a significant GFLOP reduction while maintaining comparable classification/zero-shot performance. To further validate our proposed method, we conduct continual pretraining on a large biology dataset, extending its impact into scientific domains.

Authors (2)

Yusen Peng

Sachin Kumar

Submitted

October 29, 2025

arXiv Category

cs.CV

arXiv PDF

Key Contributions

Proposes DRIP (Dynamic patch Reduction via Interpretable Pooling), a method to dynamically merge tokens in deeper layers of visual encoders, significantly reducing GFLOPs during pretraining while maintaining comparable classification and zero-shot performance. This addresses the efficiency concerns hindering the pretraining of large vision-language models from scratch.

Business Value

Enables faster and more cost-effective training of powerful multimodal AI models, democratizing access to advanced AI capabilities and accelerating research in various domains, including scientific discovery.

Paper Metadata

Innovation Type

Algorithmic Improvement / Model Compression

Deployment Feasibility

High, as it improves efficiency without sacrificing performance, making models easier to deploy and train.

Limitations Addressed

Addresses the prohibitive computational cost and efficiency concerns associated with pretraining large-scale vision-language models.

Performance Gains

Significant GFLOP reduction

Technical Tags

vision-language modelstoken mergingdynamic poolingefficiencypretrainingGFLOP reductionImageNetCLIPzero-shot performancescientific domains

Research Topics

Efficient Deep LearningMultimodal AIVision-Language ModelsModel Compression

Methods & Architectures

Dynamic patch Reduction via Interpretable Pooling (DRIP)Token mergingContrastive pretraining Vision Transformers (implied)CLIP

Applications & Tasks

General AI Research Scientific Image Analysis Computer Vision High computational cost of pretraining large vision-language modelsEfficiency concerns in deep learning models Image ClassificationZero-shot LearningPretraining Vision-Language Models

Datasets & Benchmarks

Datasets

ImageNet, Large biology dataset

Benchmarks

Significant GFLOP reduction • Comparable classification/zero-shot performance

GFLOPsClassification AccuracyZero-shot Performance

Related Fields

Deep LearningComputer VisionNatural Language ProcessingArtificial Intelligence

Keywords

vision-language modelsefficiencytoken mergingpoolingpretrainingCLIPImageNetzero-shotmodel compressiondeep learning

Academic Context

#Efficient Deep Learning#Multimodal AI#Vision-Language Models#Model Compression

Commercial Potential

Potential Products

Efficient Pretrained Vision-Language ModelsModel Compression Libraries

Target Industries

TechnologyResearch & DevelopmentBiotechnology

Use Case Examples

Faster pretraining of foundation models for multimodal AIDeveloping more efficient AI systems for edge devicesEnabling large-scale scientific image analysis

Competitive Edge

Offers a significant efficiency improvement over standard vision transformer architectures for pretraining without compromising downstream task performance.

Market Opportunity

Large market for efficient AI models and multimodal foundation models.

Revenue Models

Licensing of efficient modelsconsulting services for model optimization.

Resource Requirements

Compute Needs

Reduced computational requirements for pretraining compared to standard methods.

Data Requirements

Large-scale image datasets for pretraining (e.g., ImageNet, scientific datasets).

Deployment Constraints

Requires integration into existing model training pipelines.

Scalability

Enhances the scalability of pretraining large models.

Production Readiness

Maturity Level

Research

Time to Market

1-2 years

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