Enhancing generalization in zero-shot multi-label endoscopic instrument classification

Raphaela Maerkl, Tobias Rueckert, David Rauber, Max Gutbrod, Danilo Weber Nunes, Christoph Palm

Abstract: Recognizing previously unseen classes with neural networks is a significant challenge due to their limited generalization capabilities. This issue is particularly critical in safety-critical domains such as medical applications, where accurate classification is essential for reliability and patient safety. Zero-shot learning methods address this challenge by utilizing additional semantic data, with their performance relying heavily on the quality of the generated embeddings.