A deep active learning system for species identification and counting in camera trap images-Reference-Cited by-同舟云学术

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A deep active learning system for species identification and counting in camera trap images

Published:2020-11-20 Issue:1 Volume:12 Page:150-161
ISSN:2041-210X
Container-title:Methods in Ecology and Evolution
language:en
Short-container-title:Methods Ecol Evol

Author:

Norouzzadeh Mohammad Sadegh¹²^ORCID,Morris Dan¹,Beery Sara¹³,Joshi Neel⁴,Jojic Nebojsa⁴,Clune Jeff²⁵

Affiliation:

1. Microsoft AI for Earth Redmond WA USA

2. Computer Science Department University of Wyoming Laramie WY USA

3. Computer Science Department California Institute of Technology Pasadena CA USA

4. Microsoft Research Redmond WA USA

5. OpenAI San Francisco CA USA

Publisher

Wiley

Subject

Ecological Modelling,Ecology, Evolution, Behavior and Systematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13504

Reference47 articles.

1. AI for Earth Microsoft. (2018).Detection models. Retrieved fromhttps://github.com/Microsoft/CameraTraps

2. End‐to‐end attention‐based large vocabulary speech recognition;Bahdanau D.;2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),2016

3. Wildlife camera trapping: A review and recommendations for linking surveys to ecological processes;Burton A. C.;Journal of Applied Ecology,2015

4. Caltech Camera Traps.Caltech camera traps. Retrieved fromhttp://lila.science/datasets/caltech‐camera‐traps

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