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Found 14 papers, 7 papers with code
Presence-Only Geographical Priors for Fine-Grained Image Classification
Appearance information alone is often not sufficient to accurately differentiate between fine-grained visual categories.
The GeoLifeCLEF 2020 Dataset
Understanding the geographic distribution of species is a key concern in conservation.
The iWildCam 2020 Competition Dataset
Can we leverage data from other modalities, such as citizen science data and remote sensing data?
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.
The iWildCam 2021 Competition Dataset
The challenge is to classify species and count individual animals across sequences in the test cameras.
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.
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.
Species Distribution Modeling for Machine Learning Practitioners: A Review
How many species live there?
On Label Granularity and Object Localization
Weakly supervised object localization (WSOL) aims to learn representations that encode object location using only image-level category labels.
Teaching Computer Vision for Ecology
Computer vision can accelerate ecology research by automating the analysis of raw imagery from sensors like camera traps, drones, and satellites.
Understanding Label Bias in Single Positive Multi-Label Learning
Annotating data for multi-label classification is prohibitively expensive because every category of interest must be confirmed to be present or absent.
Spatial Implicit Neural Representations for Global-Scale Species Mapping
Estimating the geographical range of a species from sparse observations is a challenging and important geospatial prediction problem.
Active Learning-Based Species Range Estimation
Our results demonstrate that our method outperforms alternative active learning methods and approaches the performance of end-to-end trained models, even when only using a fraction of the data.
BenthIQ: a Transformer-Based Benthic Classification Model for Coral Restoration
Coral reefs are vital for marine biodiversity, coastal protection, and supporting human livelihoods globally.
