JAABA: interactive machine learning for automatic annotation of animal behavior

By Mayank Kabra, Alice Robie, M. Rivera-Alba, Steve Branson, Kristin Branson. 2013

608 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Ecology, Evolution, Behavior and Systematics, Genetics

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In Cellular and Molecular Neuroscience

In Ecology, Evolution, Behavior and Systematics

In Genetics

Citations in the corpus

Mushroom body output neurons encode valence and guide memory-based action selection in Drosophila (2014, methodology): “We also assayed the behavior of flies from each split-GAL4 driver line in a high-throughput openfield arena described in (Kabra et al., 2013) for 15 min during dTrpA1 activation using 10X UASdTRPA1 (attP16) (Hamada et al., 2008) at 30°C.”
Crowdsourcing in Computer Vision (2016, positive): “In the video domain, the Janelia Automatic Animal Behavior Annotator (JAABA) [Kabra et al., 2013]7 provides an open-source graphical interface along with an interactive active learning backend for annotating animal behavior.”
A Framework for Studying Emotions Across Phylogeny (2014, neutral): “, 2011), as well as quantitative and objective, machine vision-based methods for measuring the behavior of such model organisms (Dankert et al., 2009; Branson et al., 2009; Kabra et al., 2013).”
Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning (2020, background): “Several examples of iML systems now exist, for applications ranging from annotation of animal behavior [25], to academic citation review [58], to on-demand personalized group formations [2].”
Understanding classifier errors by examining influential neighbors (2015, methodology): “Note that JAABA allows the user to interactively add labels and retrain the classifier, thus the data are not i.i.d. Cop.”
Computational neuroethology: a call to action (2019, methodology): “JAABA, for example, includes an interface that allows researchers to indicate which video snippets correspond to a particular behavior of interest, and which can then be used to train a classifier to identify when that behavior occurred (Kabra et al., 2013).”
Applications of machine learning in animal behaviour studies (2017, positive): “ML is used to automate the classification of behaviours/activities (Kabra et al., 2013) and tracking movement trajectories (Dell et al., 2014).”
A Review of User Interface Design for Interactive Machine Learning (2018, methodology): “Researchers have demonstrated IML systems that leverage domain expertise in a range of specific applications including animal behaviour annotation (Kabra et al. 2013), insurance claims auditing (Ghani and Kumar 2011), and conducting systematic reviews of published medical evidence (Wallace et al.…”
Mapping Sub-Second Structure in Mouse Behavior. (2015, background): “Instead, traditional behavioral classification approaches have been instantiated in silico, enabling machine vision algorithms to replace tedious and unreliable human scoring of videotaped behavior (de Chaumont et al., 2012; Jhuang et al., 2010; Kabra et al., 2013; Weissbrod et al., 2013).”
Human-in-the-loop machine learning: a state of the art (2022, background): “For example, Kabra et al. (2013) use an interactive system that allows expert users, in this case biologists, to observe videos of different animals and allows them to add labels to frames in which they observe certain animals’ behaviors.”