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Title: Interpreting Expert Annotation Differences in Animal Behavior
Hand-annotated data can vary due to factors such as subjective differences, intra-rater variability, and differing annotator expertise. We study annotations from different ex- perts who labelled the same behavior classes on a set of an- imal 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 behav- ioral neuroscience demonstrate that compared to baseline approaches, our method is more accurate at capturing an- notator 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.
Authors:
; ; ;
Award ID(s):
1918865
Publication Date:
NSF-PAR ID:
10325786
Journal Name:
CVPR 2021 Workshop on CV4Animation
Sponsoring Org:
National Science Foundation
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