1 citations · 3 across the 5 of their papers we have counts for
6 papers · 1 filter
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…
Visualizing How Embeddings Generalize
Xiaotong Liu, Hong Xuan, Zeyu Zhang +2
Deep metric learning is often used to learn an embedding function that captures the semantic differences within a dataset. A key factor in many problem domains is how this embeddin…
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…
Visualizing Deep Similarity Networks
Abby Stylianou, Richard Souvenir, Robert Pless
For convolutional neural network models that optimize an image embedding, we propose a method to highlight the regions of images that contribute most to pairwise similarity. This w…