# OSSAR: Towards Open-Set Surgical Activity Recognition in Robot-assisted Surgery arXiv: https://arxiv.org/abs/2402.06985 ยท HTML: https://mlsanity.com/paper/2402.06985/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 46 references that are in the map. - First posted on arXiv 10 Feb 2024. ## Builds on (most cited first, 30 of 30) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015) - [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015) - [Regularizing Neural Networks by Penalizing Confident Output Distributions](https://mlsanity.com/paper/1701.06548/) (2017) - [Open-Set Recognition: a Good Closed-Set Classifier is All You Need?](https://mlsanity.com/paper/2110.06207/) (2021) - [Generative OpenMax for Multi-Class Open Set Classification](https://mlsanity.com/paper/1707.07418/) (2017) - [Adversarial Reciprocal Points Learning for Open Set Recognition](https://mlsanity.com/paper/2103.00953/) (2021) - [Classification-Reconstruction Learning for Open-Set Recognition](https://mlsanity.com/paper/1812.04246/) (2018) - [Surgical Data Science: Enabling Next-Generation Surgery](https://mlsanity.com/paper/1701.06482/) (2017) - [Learning Open Set Network with Discriminative Reciprocal Points](https://mlsanity.com/paper/2011.00178/) (2020) - [Surgical Data Science -- from Concepts toward Clinical Translation](https://mlsanity.com/paper/2011.02284/) (2020) - [Evidential Deep Learning for Open Set Action Recognition](https://mlsanity.com/paper/2107.10161/) (2021) - [The Devil is in the Margin: Margin-based Label Smoothing for Network Calibration](https://mlsanity.com/paper/2111.15430/) (2021) - [Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis](https://mlsanity.com/paper/1907.06099/) (2019) - [Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark](https://mlsanity.com/paper/2109.14956/) (2021) - [Surgical-VQLA: Transformer with Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery](https://mlsanity.com/paper/2305.11692/) (2023) - [Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation](https://mlsanity.com/paper/2203.15251/) (2022) - [Difficulty-Aware Simulator for Open Set Recognition](https://mlsanity.com/paper/2207.10024/) (2022) - [Towards Generalizable Diabetic Retinopathy Grading in Unseen Domains](https://mlsanity.com/paper/2307.04378/) (2023) - [Learning Robust Representation for Joint Grading of Ophthalmic Diseases via Adaptive Curriculum and Feature Disentanglement](https://mlsanity.com/paper/2207.04183/) (2022) - [Rethinking Exemplars for Continual Semantic Segmentation in Endoscopy Scenes: Entropy-based Mini-Batch Pseudo-Replay](https://mlsanity.com/paper/2308.14100/) (2023) - [CAT-ViL: Co-Attention Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery](https://mlsanity.com/paper/2307.05182/) (2023) - [LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion](https://mlsanity.com/paper/2307.02452/) (2023) - [Revisiting Distillation for Continual Learning on Visual Question Localized-Answering in Robotic Surgery](https://mlsanity.com/paper/2307.12045/) (2023) - [Rethinking Anticipation Tasks: Uncertainty-aware Anticipation of Sparse Surgical Instrument Usage for Context-aware Assistance](https://mlsanity.com/paper/2007.00548/) (2020) - [Angular Gap: Reducing the Uncertainty of Image Difficulty through Model Calibration](https://mlsanity.com/paper/2207.08525/) (2022) - [Domain Adaptive Sim-to-Real Segmentation of Oropharyngeal Organs Towards Robot-assisted Intubation](https://mlsanity.com/paper/2305.11686/) (2023) - [Semantic segmentation of surgical hyperspectral images under geometric domain shifts](https://mlsanity.com/paper/2303.10972/) (2023) - [Unsupervised Domain Adaptation for Anatomical Landmark Detection](https://mlsanity.com/paper/2308.13286/) (2023) ## Built on by (most cited first, 7 of 7) - [CoPESD: A Multi-Level Surgical Motion Dataset for Training Large Vision-Language Models to Co-Pilot Endoscopic Submucosal Dissection](https://mlsanity.com/paper/2410.07540/) (2024) - [SurgVidLM: Towards Multi-grained Surgical Video Understanding with Large Language Model](https://mlsanity.com/paper/2506.17873/) (2025) - [EndoOOD: Uncertainty-aware Out-of-distribution Detection in Capsule Endoscopy Diagnosis](https://mlsanity.com/paper/2402.11476/) (2024) - [Surgical Action Planning with Large Language Models](https://mlsanity.com/paper/2503.18296/) (2025) - [Multimodal Graph Representation Learning for Robust Surgical Workflow Recognition with Adversarial Feature Disentanglement](https://mlsanity.com/paper/2505.01766/) (2025) - [EndoARSS: Adapting Spatially-Aware Foundation Model for Efficient Activity Recognition and Semantic Segmentation in Endoscopic Surgery](https://mlsanity.com/paper/2506.06830/) (2025) - [Geo-RepNet: Geometry-Aware Representation Learning for Surgical Phase Recognition in Endoscopic Submucosal Dissection](https://mlsanity.com/paper/2507.09294/) (2025) ## Abstract (arXiv) 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.