Audio-visual Segmentation (AVS) is conceptualized as a conditional generation task, where audio is considered as the conditional variable for segmenting the sound producer(s). In this case, audio ...

A team of students from the LSU Shreveport Artificial Intelligence and Machine Learning Lab won first place at the national AIM-AHEAD annual meeting for their research poster on automatic spine ...

Machines are rapidly gaining the ability to perceive, interpret and interact with the visual world in ways that were once ...

Recently, Hongwei Li’s team from China University of Geosciences published a research article titled “scSCC: A swapped contrastive learning-based clustering method for single-cell gene ...

Semi-supervised learning (SSL) enables the accurate segmentation of medical images with limited available labeled data. However, its performance usually lags fully supervised methods that require the ...

Much prior art was dedicated to domain-adaptive semantic segmentation in the synthetic-to-real context. Despite being a crucial output of the perception stack, panoptic segmentation has been largely ...

Scientists at Massachusetts Institute of Technology have devised a way for large language models to keep learning on the fly—a step toward building AI that continually improves itself.

More information: Yuqiao Yang et al, Patch-Based Deep-Learning Model With Limited Training Dataset for Liver Tumor Segmentation in Contrast-Enhanced Hepatic Computed Tomography, IEEE Access (2025).

After uncovering a unifying algorithm that links more than 20 common machine-learning approaches, researchers organized them into a 'periodic table of machine learning' that can help ...

In this study, we propose MOLGAECL, a novel approach based on graph autoencoder pretraining and molecular graph contrastive learning. Initially, a large number of unlabeled molecular graphs are ...