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.
It is very challenging for various visual tasks such as image fusion, pedestrian detection and image-to-image translation in low light conditions due to the loss of effective target areas. We believe...
Multi-modal reasoning systems rely on a pre-trained object detector to extract regions of interest from the image. Our approach can be easily extended for visual question answering, achieving competit...
Transformer architectures have become the model of choice in natural language processing and are now being introduced into computer vision tasks such as image classification, object detection, and sem...
Monocular 3D object detection is an important task for autonomous driving considering its advantage of low cost. Our solution achieves 1st place out of all the vision-only methods in the nuScenes 3D d...
Self-supervised pretraining followed by supervised fine-tuning has seen success in image recognition, especially when labeled examples are scarce, but has received limited attention in medical image a...
This research explores Differential abundance testing on single-cell data using k-n..., contributing new insights to the field of Artificial Intelligence.
Deep generative models are a class of techniques that train deep neural networks to model the distribution of training samples. In particular, this compendium covers energy-based models, variational a...
This research explores Data quality of platforms and panels for online behavioral r..., contributing new insights to the field of Artificial Intelligence.
Abstract Precipitation nowcasting, the high-resolution forecasting of precipitation up to two hours ahead, supports the real-world socioeconomic needs of many sectors reliant on weather-dependent deci...
Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. We a...
Abstract Background The use of chatbots as learning assistants is receiving increasing attention in language learning due to their ability to converse with students using natural language. Several cha...
Artificial intelligence (AI) is a field of study that combines the applications of machine learning, algorithm productions, and natural language processing. To address these issues, this paper (1) bri...
BACKGROUND: Single group data present unique challenges for synthesises of evidence. We provide worked examples of how proportional meta-analyses have been conducted in research syntheses previously a...
Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. For...
SAR Ship Detection Dataset (SSDD) is the first open dataset that is widely used to research advanced technology of ship detection from Synthetic Aperture Radar (SAR) imagery based on deep learning (DL...
This research explores guide to machine learning for biologists, contributing new insights to the field of Artificial Intelligence.
Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. The programming and statistical background covered in the book are kept at...
This research explores BrainGNN: Interpretable Brain Graph Neural Network for fMRI..., contributing new insights to the field of Artificial Intelligence.
This research explores review of wind speed and wind power forecasting with deep ne..., contributing new insights to the field of Artificial Intelligence.
This research explores effects of remote work on collaboration among information wo..., contributing new insights to the field of Artificial Intelligence.