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Related Experiment Video

Updated: May 7, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Closed-loop correction reprogramming for fine-grained visual prompting.

Xueyi Zhang¹, Yuan Liao¹, Siqi Cai²

¹School of Artificial Intelligence, The Chinese University of Hong Kong (Shenzhen), China.

Neural Networks : the Official Journal of the International Neural Network Society

|May 5, 2026

Summary

This summary is machine-generated.

Closed-loop Correction Reprogramming (CCR) enhances visual reprogramming by iteratively refining attention maps. This method improves fine-grained classification accuracy with minimal parameter increase.

Keywords:

Attention modulation Fine-grained classification Parameter-efficient adaptation Visual reprogramming closed-loop correction

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Visual Reprogramming (VR) adapts models using pixel-level attention without parameter changes.
Existing VR methods struggle with fine-grained tasks due to dispersed attention in critical regions.

Purpose of the Study:

To introduce Closed-loop Correction Reprogramming (CCR) for improved fine-grained visual classification.
To enhance attention mechanisms in pre-trained models for better task adaptation.

Main Methods:

Proposed a dual-stream framework: Foundation Flow for initial attention and Correction Flow for iterative refinement.
Implemented a Proportional Adjustment Controller (PAC) to dynamically calibrate correction intensity based on error.
Utilized a closed-loop correction principle inspired by PID control theory.

Main Results:

Achieved up to 10.8% accuracy gain across 11 datasets with a 0.64% parameter increase.
Demonstrated an average improvement of 8.62% on five challenging fine-grained datasets.
CCR provides enhanced visual cues, improving discrimination in fine-grained classification tasks.

Conclusions:

CCR effectively refines attention maps, overcoming limitations of existing VR methods in fine-grained classification.
The proposed framework offers a parameter-efficient approach to enhance model adaptation and performance.
CCR shows significant potential for applications requiring precise visual discrimination.