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Summary from Deep Learning for Animal Re-Identification Workshop
Summary from Deep Learning for Animal Re-Identification Workshop
We'd like to thank everyone who presented and made it out to the WACV 2020 Animal Re-ID Workshop. We've collected the talks below for your future endevours.
gwtaylor-wacv-workshop.pdfGraham Taylor
University of Guelph
Vector Institute for Artificial Intelligence
Schneider - Camera Trap Symposisum.pdfStefan Schneider
University of Guelph
PhD Candidate
Animal Re-ID from Camera Traps - Sara BeerySara Beery
CalTech and Google
PhD Candidate
FourFrontiersInAnimalBiometrics_TiloBurghardt_2020.pdfTilo Burghardt
University of Bristol
Member of the British Machine Vision Association
Olga Moskvyak
Queensland University of Technology
PhD Candidate
Ekaterina Nepovinnykh
LUT Finland
PhD Candidate
parham_wacv_2020.pdfJason Parham
WildMe
PhD Candidate
Yifan Sun
Wesada University
Sai Ravela
MIT Department of Earth, Atmosphere, and Planetary Sciences
Poster_WACV_2020.pdfFredrich Tausch
Karlsruhe Institute of Technology
WACVW_Zebrafish_Re_ID_Poster_Haurum.pdfJoakim Haurum
Visual Analysis of People Laboratory
Introduction
Introduction
We're excited to present our animal re-identification workshop for WACV2020. Animal re-identification is a technique used to answer many fundamental ecological hypotheses related to animal behaviour and population dynamics. We believe the use of machine learning methods can aid and inspire fundamental research questions as well as open up the possibilities for hypotheses previously too difficult to test. We hope to keep our focus broad, but inclusive, so that everyone feels empowered to promote solutions to ecological questions. We highly encourage submissions which make their data publicly available
Example Topics
Example Topics
Topics that are anticipated, but are not required, include:
- Machine Learning for Human/Animal Re-Identification
- Vocal Recognition for Re-Identification
- Human/Animal Pose Estimation
- Behaviour Analysis from Audio/Video
- Data Augmentation for Limited Datasets
- Learning from Simulated Data
- Metric Learning
- One-Shot/ Zero-Shot Learning
- GPS Location Prediction
- Interpretability of AI Systems
- Public Ecological Datasets
- Best Practices for Data Collection and Curation
Call for Papers
Call for Papers
We invite submissions of short papers using machine learning to address problems in image, video, and audio re-identification. We are interested in bringing the ecological and computer vision communities together, centered around research involving deep learning methods for animal re-identification applications. We would like to encourage contributors from the machine learning community to present cutting edge research relevant to animal re-identification, whether image, video, or audio processing. We would also encourage ecologists with limited to no machine learning expertise to come and discuss their ecological research problems in an attempt to brainstorm how machine learning may be of assistance. In addition, we welcome everyone in between.
Details
Details
Submission
Submission
We call for papers of three categories: Contributed Papers, Extended Abstracts, and Proposals
See the Submission Details page for more information
Date
Date
Sunday, March 1st, 2020 from 1:00pm-6:30pm
Talks
Talks
25 minutes long allowing 5 minutes for questions
Evening
Evening
Post workshop socializing and brainstorming event for ideas related to the talks presented. We will be meeting at Base Camp Bar and Grill starting at 6:30pm. All are welcome!
Deadlines
Deadlines
Submission Deadline
Submission Deadline
December 15th, 2019
December 22nd, 2019 [EXTENDED]
Notification Deadline
Notification Deadline
January 20th, 2020
Camera Ready Papers Due
Camera Ready Papers Due
February 1st, 2020
Plans for Special Issue Media
Plans for Special Issue Media
We will maintain the workshop web page after the conference, hosting the list of accepted submissions and speakers. We will host links to the accepted submissions on the webpage. We plan to record the workshop talks and make the recording publicly available on the workshop web page and across social media platforms.
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