YoVDO

Unsupervised Cross-Domain Image Retrieval with Semantic-Attended Mixture-of-Experts

Offered By: Association for Computing Machinery (ACM) via YouTube

Tags

Information Retrieval Courses Machine Learning Courses Computer Vision Courses Unsupervised Learning Courses Neural Networks Courses Feature Extraction Courses Mixture-of-Experts Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore cutting-edge research in unsupervised cross-domain image retrieval through this 11-minute conference talk presented at SIGIR 2024. Delve into the innovative Semantic-Attended Mixture-of-Experts approach as authors Kai Wang, Jiayang Liu, Xing Xu, Jingkuan Song, Xin Liu, and Heng Tao Shen discuss their findings. Gain insights into advanced techniques for improving image retrieval across different domains without the need for supervised learning. Learn how this method leverages semantic information and expert models to enhance retrieval accuracy and efficiency in diverse visual contexts.

Syllabus

SIGIR 2024 M1.4 [fp] Unsupervised Cross-Domain Image Retrieval Semantic-Attended Mixture-of-Experts


Taught by

Association for Computing Machinery (ACM)

Related Courses

Neural Networks for Machine Learning
University of Toronto via Coursera Good Brain, Bad Brain: Basics
University of Birmingham via FutureLearn Statistical Learning with R
Stanford University via edX Machine Learning 1—Supervised Learning
Brown University via Udacity Fundamentals of Neuroscience, Part 2: Neurons and Networks
Harvard University via edX