Search Results for author:
Found 8 papers, 3 papers with code
Species Distribution Modeling for Machine Learning Practitioners: A Review
How many species live there?
Multi-Label Learning from Single Positive Labels
When the number of potential labels is large, human annotators find it difficult to mention all applicable labels for each training image.
Multi-Label Classification
Multi-Label Image Classification
+1
When Does Contrastive Visual Representation Learning Work?
Recent self-supervised representation learning techniques have largely closed the gap between supervised and unsupervised learning on ImageNet classification.
The iWildCam 2021 Competition Dataset
The challenge is to classify species and count individual animals across sequences in the test cameras.
Benchmarking Representation Learning for Natural World Image Collections
In order to facilitate progress in this area we present two new natural world visual classification datasets, iNat2021 and NeWT.
Fine-Grained Visual Categorization
General Classification
+3
The iWildCam 2020 Competition Dataset
Can we leverage data from other modalities, such as citizen science data and remote sensing data?
The GeoLifeCLEF 2020 Dataset
Understanding the geographic distribution of species is a key concern in conservation.
Presence-Only Geographical Priors for Fine-Grained Image Classification
Appearance information alone is often not sufficient to accurately differentiate between fine-grained visual categories.
