This article reviews recent advances in intelligent robotic ultrasound imaging systems. We
begin by presenting the commonly employed robotic mechanisms and control techniques in …

The goal of domain generalization (DG) is to enhance the generalization capability of the
model learned from a source domain to other unseen domains. The recently developed …

In a joint vision-language space, a text feature (eg, from" a photo of a dog") could effectively
represent its relevant image features (eg, from dog photos). Also, a recent study has …

We tackle open-world semantic segmentation, which aims at learning to segment arbitrary
visual concepts in images, by using only image-text pairs without dense annotations …

Abstract In Domain Generalization (DG) settings, models trained independently on a given
set of training domains have notoriously chaotic performance on distribution shifted test …

Abstract Deep Neural Networks have exhibited considerable success in various visual tasks.
However, when applied to unseen test datasets, state-of-the-art models often suffer …

In recent years, prompt tuning has proven effective in adapting pre-trained vision-language
models to down-stream tasks. These methods aim to adapt the pre-trained models by …

Detecting out-of-distribution (OOD) samples are crucial for machine learning models
deployed in open-world environments. Classifier-based scores are a standard approach for …

Abstract Domain generalization studies the problem of training a model with samples from
several domains (or distributions) and then testing the model with samples from a new …

Abstract Domain Generalization (DG) techniques have emerged as a popular approach to
address the challenges of domain shift in Deep Learning (DL), with the goal of generalizing …