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Updated: Sep 7, 2025
07:12
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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Progressive Language-Customized Visual Feature Learning for One-Stage Visual Grounding
Summary
This study introduces Progressive Language-customized Visual feature learning (PLV) for visual grounding. PLV integrates linguistic guidance early in visual feature extraction, improving accuracy and speed.
Area of Science:
- Computer Vision
- Natural Language Processing
- Artificial Intelligence
Background:
- Visual grounding localizes sentence-described objects in images.
- Current methods often separate visual and linguistic feature extraction, limiting cross-modal interaction.
- This separation may underutilize information from both modalities.
Purpose of the Study:
- To propose a novel language-customized visual feature learning mechanism.
- To enhance cross-modal interaction during feature extraction for visual grounding.
- To develop a more effective and efficient visual grounding framework.
Main Methods:
- Introduced Progressive Language-customized Visual feature learning (PLV), a one-stage framework.
- Developed a Progressive Language-customized Visual Encoder (PLVE) guided by linguistic information.
- Utilized Channel-wise Language-guided Interaction Modules (CLIM) for stage-wise visual feature customization.
Main Results:
- Achieved state-of-the-art performance on five visual grounding datasets.
- Demonstrated significant performance improvements over conventional methods.
- Realized real-time processing speeds without pre-training on object detection datasets.
Conclusions:
- The proposed PLV framework effectively integrates linguistic guidance for improved visual grounding.
- Early cross-modal interaction during feature extraction is crucial for optimal performance.
- PLV offers a promising, efficient, and accurate solution for visual grounding tasks.
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