Accurate surgical instrument segmentation is essential in cataract surgery for tasks such as skill assessment and workflow optimization. However, limited annotated data makes it difficult to develop fully automatic models. Prompt-based methods like SAM2 offer flexibility yet remain highly sensitive to the point prompt placement, often leading to inconsistent segmentations. We address this issue by introducing RP-SAM2, which incorporates a novel shift block and a compound loss function to stabilize point prompts. Our approach reduces annotator reliance on precise point positioning while maintaining robust segmentation capabilities. Experiments on the Cataract1k dataset demonstrate that RP-SAM2 improves segmentation accuracy, with a 2% mDSC gain, a 21.36% reduction in mHD95, and decreased variance across random single-point prompt results compared to SAM2. Additionally, on the CaDIS dataset, pseudo masks generated by RP-SAM2 for fine-tuning SAM2's mask decoder outperformed those generated by SAM2.

Weaknesses: In my opinion, comparing Nystrom for kernel ridge regression to variational GPs is apples to oranges in a lot of ways that are frankly unfair to variational GPs. In my view, a much more appropriate comparison would be a KeOps based implementation of SGPR or FITC with fixed inducing points. Variational GPs introduce a very large number of parameters in the form of the variational distribution and inducing point locations that require optimization and significantly increase the total amount of time spent in optimization. Methods that train GPs through the marginal likelihood with fixed inducing locations (e.g., as in Nystrom) may have as few as 3 parameters to fit. By contrast, SVGP learns (1) a variational distribution q(u) including a variational covariance matrix, and (2) the inducing point locations.

Standard online change point detection (CPD) methods tend to have large false discovery rates as their detections are sensitive to outliers. To overcome this drawback, we propose Greedy Online Change Point Detection (GOCPD), a computationally appealing method which finds change points by maximizing the probability of the data coming from the (temporal) concatenation of two independent models. We show that, for time series with a single change point, this objective is unimodal and thus CPD can be accelerated via ternary search with logarithmic complexity. We demonstrate the effectiveness of GOCPD on synthetic data and validate our findings on real-world univariate and multivariate settings.

Deep Gaussian Processes (DGPs) (Damianou and Lawrence, 2013) are a multi-layered generalization of Gaussian Processes (GPs) that inherit the advantages of GPs, namely calibrated predictive uncertainty and data-efficient learning. This makes them attractive in domains where data is sparse, such as in medical imaging or in safety critical applications such as self driving cars. The sequential embedding of the input through stacked layers of GPs solves the issue of having to hand tune kernels for specific tasks and implicitly embeds non-stationarity in the final output. Even though DGPs can be used in conjunction with the inducing point framework introduced in Hensman et al. (2013), this does not entail tractable inference as it is the case with shallow GPs. Recent implementations using stochastic approximate inference techniques have succeeded in using DGPs in medium and large datasets (Bui et al., 2016; Salimbeni and Deisenroth, 2017; Havasi et al., 2018; Yu et al., 2019). In this work we make use of the framework introduced in Salimbeni and Deisenroth (2017). Recent work (Ustyuzhaninov et al., 2019) has questioned the validity of uncertainties present in the hidden layers of DGPs, showing that approximate inference schemes using variational Gaussian distributions result in all but the last GP collapsing to deterministic transformations in the case of noiseless data. Such pathological behaviour should be avoided as it undermines the utility of layered GPs. In this paper we further investigate the status of hidden layer uncertainties in DGP, showing failure cases and we propose a solution by reinterpreting already existing models in Wasserstein-2 space.