1 citations · 3 across the 5 of their papers we have counts for
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Dissecting the impact of different loss functions with gradient surgery
Hong Xuan, Robert Pless
Pair-wise loss is an approach to metric learning that learns a semantic embedding by optimizing a loss function that encourages images from the same semantic class to be mapped clo…
Hard negative examples are hard, but useful
Hong Xuan, Abby Stylianou, Xiaotong Liu +1
Triplet loss is an extremely common approach to distance metric learning. Representations of images from the same class are optimized to be mapped closer together in an embedding s…
TraffickCam: Explainable Image Matching For Sex Trafficking Investigations
Abby Stylianou, Richard Souvenir, Robert Pless
Investigations of sex trafficking sometimes have access to photographs of victims in hotel rooms. These images directly link victims to places, which can help verify where victims…
Learning Geo-Temporal Image Features
Menghua Zhai, Tawfiq Salem, Connor Greenwell +3
We propose to implicitly learn to extract geo-temporal image features, which are mid-level features related to when and where an image was captured, by explicitly optimizing for a…
Improved Embeddings with Easy Positive Triplet Mining
Hong Xuan, Abby Stylianou, Robert Pless
Deep metric learning seeks to define an embedding where semantically similar images are embedded to nearby locations, and semantically dissimilar images are embedded to distant loc…
Hotels-50K: A Global Hotel Recognition Dataset
Abby Stylianou, Hong Xuan, Maya Shende +3
Recognizing a hotel from an image of a hotel room is important for human trafficking investigations. Images directly link victims to places and can help verify where victims have b…