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Updated: Jan 9, 2026
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Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
Published on: April 11, 2025
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Write Sentence with Images: Revisit the Large Vision Model with Visual Sentence
Quan Liu1, Can Cui1, Ruining Deng1
1Department of Computer Science, Vanderbilt University, Nashville, TN.
IS&T International Symposium on Electronic Imaging
|December 1, 2025
Summary
This study presents an efficient image generation framework using visual sentences from videos. It balances high-quality image synthesis with reduced computational cost, suitable for real-time applications.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background:
- Generating high-quality images from video sequences is computationally intensive.
- Existing methods often require significant resources and struggle with partially annotated data.
Purpose of the Study:
- To introduce a novel, efficient framework for image generation from visual sentences.
- To achieve a balance between image fidelity and computational efficiency.
Main Methods:
- A lightweight autoregressive model combined with a Vector Quantized Generative Adversarial Network (VQGAN).
- Efficiently capturing sequential patterns from partially annotated video frames.
Main Results:
- State-of-the-art performance on various benchmarks.
- Reduced inference overhead, enabling real-time and resource-constrained applications.
- Demonstrated capabilities in medical image analysis (denoising, brightness adjustment, segmentation).
Conclusions:
- The proposed framework offers an effective balance between performance and efficiency.
- Paves the way for scalable and adaptive image generation in multimedia and medical imaging.
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