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In-Situ Fine-Tuning of Wildlife Models in IoT-Enabled Camera Traps for Efficient Adaptation
Wildlife monitoring via camera traps has become an essential tool in ecology, but the deployment of machine learning models for on-device animal classification faces significant challenges due to domain shifts and resource constraints.
Efficient IoT Inference via Context-Awareness
While existing strategies to execute deep learning-based classification on low-power platforms assume the models are trained on all classes of interest, this paper posits that adopting context-awareness i. e. narrowing down a classification task to the current deployment context consisting of only recent inference queries can substantially enhance performance in resource-constrained environments.
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