1 citations · 2 across the 3 of their papers we have counts for
7 papers
Dissecting Deep Metric Learning Losses for Image-Text Retrieval
Hong Xuan, Xi Chen
Visual-Semantic Embedding (VSE) is a prevalent approach in image-text retrieval by learning a joint embedding space between the image and language modalities where semantic similar…
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…
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…