Search Results for author:
Found 2 papers, 1 papers with code
Unsupervised Domain Adaptation in the Dissimilarity Space for Person Re-identification
We argue that for pair-wise matchers that rely on metric learning, e. g., Siamese networks for person ReID, the unsupervised domain adaptation (UDA) objective should consist in aligning pair-wise dissimilarity between domains, rather than aligning feature representations.
Dual-Triplet Metric Learning for Unsupervised Domain Adaptation in Video-Based Face Recognition
To this end, a dual-triplet loss is introduced for metric learning, where two triplets are constructed using video data from a source camera, and a new target camera.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
