  In minimally invasive surgery, surgical instrument localization is a crucial
task for endoscopic videos, which enables various applications for improving
surgical outcomes. However, annotating the instrument localization in
endoscopic videos is tedious and labor-intensive. In contrast, obtaining the
category information is easy and efficient in real-world applications. To fully
utilize the category information and address the localization problem, we
propose a weakly supervised localization framework named WS-YOLO for surgical
instruments. By leveraging the instrument category information as the weak
supervision, our WS-YOLO framework adopts an unsupervised multi-round training
strategy for the localization capability training. We validate our WS-YOLO
framework on the Endoscopic Vision Challenge 2023 dataset, which achieves
remarkable performance in the weakly supervised surgical instrument
localization. The source code is available at
https://github.com/Breezewrf/WS-YOLO.
