Latest AI and machine learning research in covid-19 for healthcare professionals.
Over more than a decade there has been an extensive research effort on how to effectively utilize recurrent models and attention. While recurrent models aim to compress the data into a fixed-size memory (called hidden state), attention allows attending to the entire context window, capturing the direct dependencies of all tokens. This more accurate modeling of dependencies, however, comes with a...
Spatiotemporal prediction over graphs (STPG) is challenging, because real-world data suffers from the Out-of-Distribution (OOD) generalization problem, where test data follow different distributions from training ones. To address this issue, Invariant Risk Minimization (IRM) has emerged as a promising approach for learning invariant representations across different environments. However, IRM and...
The primary challenge of cross-domain few-shot segmentation (CD-FSS) is the domain disparity between the training and inference phases, which can ex...
Text-guided image editing model has achieved great success in general domain. However, directly applying these models to the fashion domain may enco...
Diffusion-based text-to-image (T2I) models have demonstrated remarkable results in global video editing tasks. However, their focus is primarily on ...
Recent research in subject-driven generation increasingly emphasizes the importance of selective subject features. Nevertheless, accurately selectin...
Dichotomous Image Segmentation (DIS) tasks require highly precise annotations, and traditional dataset creation methods are labor intensive, costly,...
Obtaining an explicit understanding of communication within a Hybrid Intelligence collaboration is essential to create controllable and transparent ...
Graph Mamba, a powerful graph embedding technique, has emerged as a cornerstone in various domains, including bioinformatics, social networks, and r...
Existing open-vocabulary object detection (OVD) develops methods for testing unseen categories by aligning object region embeddings with correspondi...
Extracting medication names from handwritten doctor prescriptions is challenging due to the wide variability in handwriting styles and prescription ...
Recent virtual try-on approaches have advanced by fine-tuning the pre-trained text-to-image diffusion models to leverage their powerful generative a...
Few-shot defect multi-classification (FSDMC) is an emerging trend in quality control within industrial manufacturing. However, current FSDMC researc...
Forensic science plays a crucial role in legal investigations, and the use of advanced technologies, such as object detection based on machine learn...
Noninvasive human-machine interfaces such as surface electromyography (sEMG) have long been employed for controlling robotic prostheses. However, cl...
Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most exis...
Local reconstruction analysis (LRA) is a powerful and flexible technique to study images reconstructed from discrete generalized Radon transform (GR...
As a cost-effective and robust technology, automotive radar has seen steady improvement during the last years, making it an appealing complement to ...
Current saliency-based defect detection methods show promise in industrial settings, but the unpredictability of defects in steel production environ...
3D color lookup tables (LUTs) enable precise color manipulation by mapping input RGB values to specific output RGB values. 3D LUTs are instrumental ...