Evaluating Few-Shot Pill Recognition Under Visual Domain Shift

• URL: http://arxiv.org/abs/2603.10833v1
• Date: Wed, 11 Mar 2026 14:40:55 GMT
• Title: Evaluating Few-Shot Pill Recognition Under Visual Domain Shift
• Abstract summary: This study investigates few-shot pill recognition from a deployment-oriented perspective.<n>A two-stage object detection framework is employed, involving base training followed by few-shot fine-tuning.<n>Models trained on visually realistic, multi-pill data consistently exhibit greater robustness in low-shot scenarios.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Adverse drug events are a significant source of preventable harm, which has led to the development of automated pill recognition systems to enhance medication safety. Real-world deployment of these systems is hindered by visually complex conditions, including cluttered scenes, overlapping pills, reflections, and diverse acquisition environments. This study investigates few-shot pill recognition from a deployment-oriented perspective, prioritizing generalization under realistic cross-dataset domain shifts over architectural innovation. A two-stage object detection framework is employed, involving base training followed by few-shot fine-tuning. Models are adapted to novel pill classes using one, five, or ten labeled examples per class and are evaluated on a separate deployment dataset featuring multi-object, cluttered scenes. The evaluation focuses on classification-centric and error-based metrics to address heterogeneous annotation strategies. Findings indicate that semantic pill recognition adapts rapidly with few-shot supervision, with classification performance reaching saturation even with a single labeled example. However, stress testing under overlapping and occluded conditions demonstrates a marked decline in localization and recall, despite robust semantic classification. Models trained on visually realistic, multi-pill data consistently exhibit greater robustness in low-shot scenarios, underscoring the importance of training data realism and the diagnostic utility of few-shot fine-tuning for deployment readiness.

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