Coordinative Learning with Ordinal and Relational Priors for Volumetric Medical Image Segmentation

The paper titled 'Laplace Learning in Wasserstein Space' explores the manifold hypothesis, which suggests that high-dimensional data typically exists within low-dimensional subspaces. The authors investigate graph-based semi-supervised learning methods, particularly focusing on Laplace Learning in the Wasserstein space. This research extends classical graph-based learning algorithms from finite-dimensional Euclidean spaces to infinite-dimensional contexts. The study proves the variational convergence of discrete graph p-Dirichlet energy to its continuum counterpart and characterizes the Laplac…

The paper discusses the challenges of test-time prompt tuning for vision-language models, highlighting the issue of prompt optimization bias that can lead to suboptimal performance in downstream tasks. It identifies two main causes: the model's focus on entropy minimization, which may overlook prediction accuracy, and data misalignment between visual and textual modalities. To address these issues, the authors propose a new method called Doubly Debiased Test-Time Prompt Tuning, aimed at improving model performance in zero-shot settings.

The article presents FlowPath, a novel method for modeling continuous-time dynamics from sparse and irregularly-sampled time series. It addresses the limitations of existing methods that rely on fixed interpolation schemes, which can misrepresent data. FlowPath utilizes an invertible neural flow to create a continuous, data-adaptive manifold, enhancing classification accuracy significantly across 18 benchmark datasets.