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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.
Once through the pupil, the light passes through the lens, a...

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Related Experiment Video

Updated: May 10, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Unified Domain Adaptive Semantic Segmentation.

Zhe Zhang, Gaochang Wu, Jing Zhang

IEEE Transactions on Pattern Analysis and Machine Intelligence

|April 21, 2025

Summary

This summary is machine-generated.

This study unifies Unsupervised Domain Adaptive Semantic Segmentation (UDA-SS) for images and videos. A novel Quad-directional Mixup (QuadMix) method improves cross-domain adaptation and performance on segmentation tasks.

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Last Updated: May 10, 2025

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Unsupervised Domain Adaptive Semantic Segmentation (UDA-SS) addresses challenges in transferring models to new data domains.
Existing UDA-SS research often focuses on either images or videos independently, leading to fragmented knowledge and missed opportunities.

Purpose of the Study:

To unify the study of UDA-SS across image and video domains for comprehensive understanding and synergistic advancements.
To develop a general domain augmentation framework for improved generalization and cross-pollination of ideas.

Main Methods:

Propose Quad-directional Mixup (QuadMix) for intra- and inter-domain mixing in feature space.
Incorporate optical flow-guided feature aggregation for temporal shift alignment in videos, extendable to images.

Main Results:

QuadMix effectively bridges domain gaps and addresses feature inconsistencies.
The proposed method achieves state-of-the-art performance on four challenging UDA-SS benchmarks.

Conclusions:

Unifying UDA-SS research for images and videos fosters holistic understanding and efficient knowledge sharing.
The QuadMix method offers a promising direction for robust domain adaptation in semantic segmentation.