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Found 11 papers, 7 papers with code
Vision-Language Pseudo-Labels for Single-Positive Multi-Label Learning
In general multi-label learning, a model learns to predict multiple labels or categories for a single input image.
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.
What Does TERRA-REF's High Resolution, Multi Sensor Plant Sensing Public Domain Data Offer the Computer Vision Community?
This sensor data is provided alongside over sixty types of traditional plant phenotype measurements that can be used to train new machine learning models.
Multi-resolution Outlier Pooling for Sorghum Classification
Automated high throughput plant phenotyping involves leveraging sensors, such as RGB, thermal and hyperspectral cameras (among others), to make large scale and rapid measurements of the physical properties of plants for the purpose of better understanding the difference between crops and facilitating rapid plant breeding programs.
The 2021 Hotel-ID to Combat Human Trafficking Competition Dataset
Hotel recognition is an important task for human trafficking investigations since victims are often photographed in hotel rooms.
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.
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.
