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Found 4 papers, 3 papers with code
Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit Anomalies
Data augmentation can be a simple yet powerful tool for autonomous robots to fully utilise available data for selfsupervised identification of atypical scenes or objects.
Beyond Tracking: Using Deep Learning to Discover Novel Interactions in Biological Swarms
Because the resulting predictive models are not based on human-understood predictors, we use explanatory modules (e. g., Grad-CAM) that combine information hidden in the latent variables of the deep-network model with the video data itself to communicate to a human observer which aspects of observed individual behaviors are most informative in predicting group behavior.
Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning
In our previous work, we designed a systematic policy to prioritize sampling locations to lead significant accuracy improvement in spatial interpolation by using the prediction uncertainty of Gaussian Process Regression (GPR) as "attraction force" to deployed robots in path planning.
Identification of Abnormal States in Videos of Ants Undergoing Social Phase Change
This method can be used to screen video frames for which additional human observation is needed.
