  In the realm of automated robotic surgery and computer-assisted
interventions, understanding robotic surgical activities stands paramount.
Existing algorithms dedicated to surgical activity recognition predominantly
cater to pre-defined closed-set paradigms, ignoring the challenges of
real-world open-set scenarios. Such algorithms often falter in the presence of
test samples originating from classes unseen during training phases. To tackle
this problem, we introduce an innovative Open-Set Surgical Activity Recognition
(OSSAR) framework. Our solution leverages the hyperspherical reciprocal point
strategy to enhance the distinction between known and unknown classes in the
feature space. Additionally, we address the issue of over-confidence in the
closed set by refining model calibration, avoiding misclassification of unknown
classes as known ones. To support our assertions, we establish an open-set
surgical activity benchmark utilizing the public JIGSAWS dataset. Besides, we
also collect a novel dataset on endoscopic submucosal dissection for surgical
activity tasks. Extensive comparisons and ablation experiments on these
datasets demonstrate the significant outperformance of our method over existing
state-of-the-art approaches. Our proposed solution can effectively address the
challenges of real-world surgical scenarios. Our code is publicly accessible at
https://github.com/longbai1006/OSSAR.
