114 citations · 131 across the 5 of their papers we have counts for
9 papers
Multi-Label Learning from Single Positive Labels
Elijah Cole, Oisin Mac Aodha, Titouan Lorieul +3
Predicting all applicable labels for a given image is known as multi-label classification. Compared to the standard multi-class case (where each image has only one label), it is co…
Sequence Information Channel Concatenation for Improving Camera Trap Image Burst Classification
Bhuvan Malladihalli Shashidhara, Darshan Mehta, Yash Kale +2
Camera Traps are extensively used to observe wildlife in their natural habitat without disturbing the ecosystem. This could help in the early detection of natural or human threats…
The GeoLifeCLEF 2020 Dataset
Elijah Cole, Benjamin Deneu, Titouan Lorieul +6
Understanding the geographic distribution of species is a key concern in conservation. By pairing species occurrences with environmental features, researchers can model the relatio…
Local Context Normalization: Revisiting Local Normalization
Anthony Ortiz, Caleb Robinson, Dan Morris +4
Normalization layers have been shown to improve convergence in deep neural networks, and even add useful inductive biases. In many vision applications the local spatial context of…
A deep active learning system for species identification and counting in camera trap images
Mohammad Sadegh Norouzzadeh, Dan Morris, Sara Beery +3
Biodiversity conservation depends on accurate, up-to-date information about wildlife population distributions. Motion-activated cameras, also known as camera traps, are a critical…
The iWildCam 2019 Challenge Dataset
Sara Beery, Dan Morris, Pietro Perona
Camera Traps (or Wild Cams) enable the automatic collection of large quantities of image data. Biologists all over the world use camera traps to monitor biodiversity and population…