Divide And Conquer The Embedding Space For Metric Learning

Abstract

Learning the embedding space, where semantically similar objects are located close together and dissimilar objects far apart, is a cornerstone of many computer vision applications. Existing approaches usually learn a single metric in the embedding space for all available data points, which may have a very complex non-uniform distribution with different notions of similarity between objects, e.g. appearance, shape, color or semantic meaning. Approaches for learning a single distance metric often struggle to encode all different types of relationships and do not generalize well. In this work, we propose a novel easy-to-implement divide and conquer approach for deep metric learning, which significantly improves the state-of-the-art performance of metric learning. Our approach utilizes the embedding space more efficiently by jointly splitting the embedding space and data into \(K\) smaller sub-problems. It divides both, the data and the embedding space into \(K\) subsets and learns \(K\) s

Stats

• citations82
• S2 citations—
• github stars0
• HF likes0
• heat score14.39
• arxiv keysanakoyeu2019divide

Related papers

• Improving Deep Metric Learning By Divide And Conquer (2021)8.09
• Deep Metric Learning For Computer Vision: A Brief Overview (2023)6.77
• Metric Learning As A Service With Covariance Embedding (2022)3.58
• Learning To Embed Semantic Similarity For Joint Image-text Retrieval (2022)7.50
• Deep Metric Learning Assisted By Intra-variance In A Semi-supervised View Of Learning (2023)5.24
• Generalization In Metric Learning: Should The Embedding Layer Be The Embedding Layer? (2018)8.60
• A New Similarity Space Tailored For Supervised Deep Metric Learning (2020)3.58
• Learning Intra-batch Connections For Deep Metric Learning (2021)0.00