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Found 17 papers, 8 papers with code
Dissecting the impact of different loss functions with gradient surgery
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 closer than images from different classes.
Ranked #7 on
Metric Learning
on In-Shop
Classification and Visualization of Genotype x Phenotype Interactions in Biomass Sorghum
We introduce a simple approach to understanding the relationship between single nucleotide polymorphisms (SNPs), or groups of related SNPs, and the phenotypes they control.
DCAP: Deep Cross Attentional Product Network for User Response Prediction
Prior studies in predicting user response leveraged the feature interactions by enhancing feature vectors with products of features to model second-order or high-order cross features, either explicitly or implicitly.
Hard negative examples are hard, but useful
We offer a simple fix to the loss function and show that, with this fix, optimizing with hard negative examples becomes feasible.
Ranked #14 on
Metric Learning
on In-Shop
TraffickCam: Explainable Image Matching For Sex Trafficking Investigations
Investigations of sex trafficking sometimes have access to photographs of victims in hotel rooms.
Extreme Triplet Learning: Effectively Optimizing Easy Positives and Hard Negatives
The Triplet Loss approach to Distance Metric Learning is defined by the strategy to select triplets and the loss function through which those triplets are optimized.
Learning Geo-Temporal Image Features
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 set of location and time estimation tasks.
Visualizing How Embeddings Generalize
Deep metric learning is often used to learn an embedding function that captures the semantic differences within a dataset.
Improved Embeddings with Easy Positive Triplet Mining
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 locations.
Ranked #6 on
Image Retrieval
on In-Shop
Hotels-50K: A Global Hotel Recognition Dataset
Recognizing a hotel from an image of a hotel room is important for human trafficking investigations.
Visualizing Deep Similarity Networks
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.
Deep Randomized Ensembles for Metric Learning
Learning embedding functions, which map semantically related inputs to nearby locations in a feature space supports a variety of classification and information retrieval tasks.
Deep Feature Interpolation for Image Content Changes
We propose Deep Feature Interpolation (DFI), a new data-driven baseline for automatic high-resolution image transformation.
Single Image Camera Calibration With Lenticular Arrays for Augmented Reality
We consider the problem of camera pose estimation for a scenario where the camera may have continuous and unknown changes in its focal length.
Building Dynamic Cloud Maps From the Ground Up
Satellite imagery of cloud cover is extremely important for understanding and predicting weather.
The Episolar Constraint: Monocular Shape from Shadow Correspondence
For outdoor scenes with solar illumination, we term this the episolar constraint, which provides a convex optimization to solve for the sparse depth of a scene from shadow correspondences, a method to reduce the search space when finding shadow correspondences, and a method to geometrically calibrate a camera using shadow constraints.
Shadow Estimation Method for "The Episolar Constraint: Monocular Shape from Shadow Correspondence"
We show these approaches only work with very careful tuning of parameters, and do not work well for long-term time-lapse sequences taken over the span of many months.
