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Found 6 papers, 6 papers with code
Introspective Deep Metric Learning
This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-aware comparisons of images.
Deep Factorized Metric Learning
Learning a generalizable and comprehensive similarity metric to depict the semantic discrepancies between images is the foundation of many computer vision tasks.
Probabilistic Deep Metric Learning for Hyperspectral Image Classification
We propose to learn a global probabilistic distribution for each pixel in the patch and a probabilistic metric to model the distance between distributions.
OPERA: Omni-Supervised Representation Learning with Hierarchical Supervisions
The pretrain-finetune paradigm in modern computer vision facilitates the success of self-supervised learning, which tends to achieve better transferability than supervised learning.
Introspective Deep Metric Learning for Image Retrieval
This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-aware comparisons of images.
Deep Compositional Metric Learning
In this paper, we propose a deep compositional metric learning (DCML) framework for effective and generalizable similarity measurement between images.
