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Vision

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

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Related Experiment Video

Updated: Feb 20, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

VL-HTR: Learning Human-Target Representation From Vision-Language Model.

Binglu Wang, Chenxi Guo, Jingyi Cui

IEEE Transactions on Cybernetics

|February 18, 2026

Summary

This summary is machine-generated.

This study introduces VL-HTR, a new vision-language method for human-gaze-target prediction. It uses multimodal knowledge to improve accuracy and significantly speeds up training convergence for better human-target understanding.

Related Experiment Videos

Last Updated: Feb 20, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Computer Vision
Artificial Intelligence
Human-Computer Interaction

Background:

Existing human-gaze-target prediction methods often use vision-only features.
These methods struggle with small/occluded objects and lack precise head direction priors, causing slow convergence and poor performance.

Purpose of the Study:

Introduce VL-HTR, a novel vision-language learning method for human-target representation.
Integrate multimodal knowledge from vision-language models (VLMs) to build robust human-target relationships.
Enhance semantic understanding and accelerate gaze point regression.

Main Methods:

Extract multimodal features using pretrained VLMs for improved human-target knowledge.
Employ a language-guided query alignment (LQA) module for semantic-aware object representation.
Utilize a language-guided direction prediction (LDP) module for multimodal human gaze direction priors.

Main Results:

VL-HTR demonstrates superior performance on gaze object prediction (GOP) and gaze target estimation tasks.
Achieved significantly faster training convergence across five challenging benchmarks.
Validated effectiveness in constructing human-target relationships.

Conclusions:

VL-HTR effectively leverages multimodal information for enhanced human-gaze-target prediction.
The proposed method offers a more robust and efficient approach compared to vision-only techniques.
VL-HTR shows promise for advancing research in gaze analysis and human-computer interaction.