Large-Scale Metric Learning: A Voyage From Shallow to Deep
By Masoud Faraki, Mehrtash Harandi, Fatih Murat Porikli. 2018
12 citations recorded by the source corpus.
Despite its attractive properties, the performance of the recently introduced Keep It Simple and Straightforward MEtric learning (KISSME) method is greatly dependent on principal component analysis as a preprocessing step. This dependence can lead to difficulties, e.g., when the dimensionality is not meticulously set. To address this issue, we devise a unified formulation for joint dimensionality reduction and metric learning based on the KISSME algorithm. Our joint formulation is expressed as an optimization problem on the Grassmann manifold, and hence enjoys the properties of Riemannian optimization techniques. Following the success of deep learning in recent years, we also devise end-to-end learning of a generic deep network for metric learning using our derivation.
Subfields: Computer Vision and Pattern Recognition
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In Computer Vision and Pattern Recognition
- Luc Van Gool
- Manmohan Chandraker
- Yi-Hsuan Tsai
- Samuel Schulter
- Mehrtash Harandi
- Christian Simon
- Xiang Yu
- Masoud Faraki
Citations in the corpus
- On Generalizing Beyond Domains in Cross-Domain Continual Learning (2022, background): “This will ensure Σ remains PSD and yields valid similarity scores [7, 8].”
- Cross-Domain Similarity Learning for Face Recognition in Unseen Domains (2021, background): “contrastive [20] or triplet [36]) to provide the embeddings of the data to a reduced dimension space [12, 13, 36].”