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Interpreting Expert Annotation Differences in Animal Behavior

Tjandrasuwita, Megan and Sun, Jennifer J. and Kennedy, Ann and Chaudhuri, Swarat and Yue, Yisong (2021) Interpreting Expert Annotation Differences in Animal Behavior. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20220224-200758198

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Abstract

Hand-annotated data can vary due to factors such as subjective differences, intra-rater variability, and differing annotator expertise. We study annotations from different experts who labelled the same behavior classes on a set of animal behavior videos, and observe a variation in annotation styles. We propose a new method using program synthesis to help interpret annotation differences for behavior analysis. Our model selects relevant trajectory features and learns a temporal filter as part of a program, which corresponds to estimated importance an annotator places on that feature at each timestamp. Our experiments on a dataset from behavioral neuroscience demonstrate that compared to baseline approaches, our method is more accurate at capturing annotator labels and learns interpretable temporal filters. We believe that our method can lead to greater reproducibility of behavior annotations used in scientific studies. We plan to release our code.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2106.06114arXivDiscussion Paper
ORCID:
AuthorORCID
Sun, Jennifer J.0000-0002-0906-6589
Kennedy, Ann0000-0002-3782-0518
Yue, Yisong0000-0001-9127-1989
Record Number:CaltechAUTHORS:20220224-200758198
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220224-200758198
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:113576
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:25 Feb 2022 23:13
Last Modified:25 Feb 2022 23:13

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