A large amount of high-dimensional and heterogeneous data appear in prac...
Pair-wise loss is an approach to metric learning that learns a semantic
...
We introduce a simple approach to understanding the relationship between...
Triplet loss is an extremely common approach to distance metric learning...
Investigations of sex trafficking sometimes have access to photographs o...
We propose to implicitly learn to extract geo-temporal image features, w...
Deep metric learning is often used to learn an embedding function that
c...
Deep metric learning seeks to define an embedding where semantically sim...
Recognizing a hotel from an image of a hotel room is important for human...
For convolutional neural network models that optimize an image embedding...
Learning embedding functions, which map semantically related inputs to n...
We propose Deep Feature Interpolation (DFI), a new data-driven baseline ...
Recovering shadows is an important step for many vision algorithms. Curr...