Accurate instrument pose estimation is a crucial step towards the future of robotic surgery, enabling applications such as autonomous surgical task execution. Vision-based methods for surgical instrument pose estimation provide a practical approach to tool tracking, but they often require markers to be attached to the instruments. Recently, more research has focused on the development of marker-less methods based on deep learning. However, acquiring realistic surgical data, with ground truth instrument poses, required for deep learning training, is challenging. To address the issues in surgical instrument pose estimation, we introduce the Surgical Robot Instrument Pose Estimation (SurgRIPE) challenge, hosted at the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023. The objectives of this challenge are: (1) to provide the surgical vision community with realistic surgical video data paired with ground truth instrument poses, and (2) to establish a benchmark for evaluating markerless pose estimation methods. The challenge led to the development of several novel algorithms that showcased improved accuracy and robustness over existing methods. The performance evaluation study on the SurgRIPE dataset highlights the potential of these advanced algorithms to be integrated into robotic surgery systems, paving the way for more precise and autonomous surgical procedures. The SurgRIPE challenge has successfully established a new benchmark for the field, encouraging further research and development in surgical robot instrument pose estimation.

Machine Learning (ML) models struggle with data that changes over time or across domains due to factors such as noise, occlusion, illumination, or frequency, unlike humans who can learn from such non independent and identically distributed data. Consequently, a Continual Learning (CL) approach is indispensable, particularly, Domain-Incremental Learning. In this paper, we propose a novel pipeline for identifying tasks in domain-incremental learning scenarios without supervision. The pipeline comprises four steps. First, we obtain base embeddings from the raw data using an existing transformer-based model. Second, we group the embedding densities based on their similarity to obtain the nearest points to each cluster centroid. Third, we train an incremental task classifier using only these few points. Finally, we leverage the lightweight computational requirements of the pipeline to devise an algorithm that decides in an online fashion when to learn a new task using the task classifier and a drift detector. We conduct experiments using the SODA10M real-world driving dataset and several CL strategies. We demonstrate that the performance of these CL strategies with our pipeline can match the ground-truth approach, both in classical experiments assuming task boundaries, and also in more realistic task-agnostic scenarios that require detecting new tasks on-the-fly

Continual learning refers to a dynamical framework in which a model receives a stream of non-stationary data over time and must adapt to new data while preserving previously acquired knowledge. Unluckily, neural networks fail to meet these two desiderata, incurring the so-called catastrophic forgetting phenomenon. Whereas a vast array of strategies have been proposed to attenuate forgetting in the computer vision domain, for speech-related tasks, on the other hand, there is a dearth of works. In this paper, we consider the joint use of rehearsal and knowledge distillation (KD) approaches for spoken language understanding under a class-incremental learning scenario. We report on multiple KD combinations at different levels in the network, showing that combining feature-level and predictions-level KDs leads to the best results. Finally, we provide an ablation study on the effect of the size of the rehearsal memory that corroborates the efficacy of our approach for low-resource devices.