Shot Detection

Shot detection, particularly few-shot object detection, aims to accurately identify objects in images using minimal labeled training data. Current research emphasizes developing efficient and robust models, often leveraging meta-learning, transformer architectures, and prototypical networks, to overcome the challenges of limited data and improve generalization to unseen objects. This field is crucial for applications where labeled data is scarce or expensive to acquire, such as remote sensing, industrial anomaly detection, and autonomous systems, impacting various domains by enabling more efficient and adaptable object recognition. Recent work also explores semi-supervised and test-time adaptation approaches to further enhance performance and practicality.