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Found 9 papers, 5 papers with code
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.
Sequence Information Channel Concatenation for Improving Camera Trap Image Burst Classification
We hypothesize that a short burst of images instead of a single image, assuming that the animal moves, makes it much easier for a human as well as a machine to detect the presence of animals.
The GeoLifeCLEF 2020 Dataset
Understanding the geographic distribution of species is a key concern in conservation.
Local Context Normalization: Revisiting Local Normalization
In many vision applications the local spatial context of the features is important, but most common normalization schemes including Group Normalization (GN), Instance Normalization (IN), and Layer Normalization (LN) normalize over the entire spatial dimension of a feature.
A deep active learning system for species identification and counting in camera trap images
However, the accuracy of results depends on the amount, quality, and diversity of the data available to train models, and the literature has focused on projects with millions of relevant, labeled training images.
Efficient Pipeline for Camera Trap Image Review
Biologists all over the world use camera traps to monitor biodiversity and wildlife population density.
The iWildCam 2019 Challenge Dataset
We use the Caltech Camera Traps dataset, collected from the American Southwest, as training data.
Human-Machine Collaboration for Fast Land Cover Mapping
This bi-directional feedback loop allows humans to learn how the model responds to new data.
Synthetic Examples Improve Generalization for Rare Classes
The ability to detect and classify rare occurrences in images has important applications - for example, counting rare and endangered species when studying biodiversity, or detecting infrequent traffic scenarios that pose a danger to self-driving cars.
