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Visual System

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
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Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
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Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.

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Related Experiment Video

Updated: Sep 15, 2025

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

Published on: November 30, 2018

Trustworthy Visual-Textual Retrieval.

Yang Qin, Lifu Huang, Dezhong Peng

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|July 15, 2025

Summary

This summary is machine-generated.

This study introduces Trust-Consistent Learning (TCL) for more reliable visual-textual retrieval. TCL evaluates retrieval uncertainty, improving accuracy and interpretability in cross-modal learning tasks.

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Last Updated: Sep 15, 2025

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Published on: November 30, 2018

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Published on: January 10, 2014

Area of Science:

Artificial Intelligence
Computer Vision
Natural Language Processing

Background:

Visual-textual retrieval links computer vision and natural language processing.
Current methods lack uncertainty evaluation, leading to unreliable results and poor interpretability.

Purpose of the Study:

To propose a novel Trust-Consistent Learning (TCL) framework for trustworthy visual-textual retrieval.
To introduce uncertainty evaluation into visual-textual retrieval for enhanced reliability and accuracy.

Main Methods:

TCL models matching evidence using cross-modal similarity to estimate uncertainty.
A consistency module enforces agreement between bidirectional learning for improved reliability.

Main Results:

TCL demonstrates superiority and generalizability across six benchmark datasets (Flickr30K, MS-COCO, MSVD, MSR-VTT, ActivityNet, DiDeMo).
Qualitative experiments verify the reliability and interoperability of the TCL framework.

Conclusions:

The proposed TCL framework enhances visual-textual retrieval by incorporating uncertainty evaluation.
TCL offers a reliable and interpretable solution for cross-modal retrieval tasks.