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20192021
most citedEfficient Pipeline for Camera Trap Image Review

114 citations · 131 across the 5 of their papers we have counts for

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7 papers · 1 filter

cs.CV2021

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…

cs.CV20203 cited

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…

cs.CV20202 cited

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…

cs.CV2019

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…

cs.CV201912 cited

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…

cs.CV2019114 cited

Efficient Pipeline for Camera Trap Image Review

Sara Beery, Dan Morris, Siyu Yang

Biologists all over the world use camera traps to monitor biodiversity and wildlife population density. The computer vision community has been making strides towards automating the…