Explore the latest peer-reviewed research with AI-generated summaries, key findings, and insights for easy understanding. All content is legally sourced from academic metadata with links to original papers.
This paper develops a unified framework for image-to-image translation based on conditional diffusion models and evaluates this framework on four challenging image-to-image translation tasks, namely c...
This research explores CLIP4Clip: An empirical study of CLIP for end to end video c..., contributing new insights to the field of Artificial Intelligence.
The metaverse has the potential to extend the physical world using augmented and virtual reality technologies allowing users to seamlessly interact within real and simulated environments using avatars...
International challenges have become the de facto standard for comparative assessment of image analysis algorithms. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing w...
This research explores Natural language processing: state of the art, current trend..., contributing new insights to the field of Artificial Intelligence.
This research explores Artificial rabbits optimization: A new bio-inspired meta-heu..., contributing new insights to the field of Artificial Intelligence.
MOTIVATION: With the current pace at which reference genomes are being produced, the availability of tools that can reliably and efficiently generate genome assembly summary statistics has become crit...
Searching for people online is a common search task that most of us have performed at some point or other. The demonstrator also comprises a cyber-safety tool, which aims to provide education and rais...
Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been attempted for other speech processing tasks. We also scale up the training dataset from...
This research explores Activation functions in deep learning: A comprehensive surve..., contributing new insights to the field of Artificial Intelligence.
Neural graphics primitives, parameterized by fully connected neural networks, can be costly to train and evaluate. We leverage this parallelism by implementing the whole system using fully-fused CUDA...
This study proposes a novel general image fusion framework based on cross-domain long-range learning and Swin Transformer, termed as SwinFusion. Extensive experiments on both multi-modal image fusion...
Most recent semantic segmentation methods adopt a U-Net framework with an encoder-decoder architecture. The experimental results suggest that our UCTransNet produces more precise segmentation performa...
This paper presents TS2Vec, a universal framework for learning representations of time series in an arbitrary semantic level. Additionally, we present a simple way to apply the learned representations...
Heterogeneity across clients in federated learning (FL) usually hinders the optimization convergence and generalization performance when the aggregation of clients' knowledge occurs in the gradient sp...
Federated learning (FL) and split learning (SL) are two popular distributed machine learning approaches. Additionally, as in SL, its communication efficiency over FL improves with the number of client...
Single-cell transcriptomics (scRNA-seq) has become essential for biomedical research over the past decade, particularly in developmental biology, cancer, immunology, and neuroscience. Spatial -omics m...
Machine learning has shown utility in detecting patterns within large, unstructured, and complex datasets. Therefore, the generalizability of machine learning models benefits from feature selection, w...
This research explores UNetFormer: A UNet-like transformer for efficient semantic s..., contributing new insights to the field of Artificial Intelligence.
This article presents a advanced review of the applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in building and construction industry 4.0 in the facets of ar...