Latest AI and machine learning research in covid-19 for healthcare professionals.
Gaussian Splatting has become a popular technique for various 3D Computer Vision tasks, including novel view synthesis, scene reconstruction, and dynamic scene rendering. However, the challenge of natural-looking object insertion, where the object's appearance seamlessly matches the scene, remains unsolved. In this work, we propose a method, dubbed D3DR, for inserting a 3DGS-parametrized object ...
With transformer-based models and the pretrain-finetune paradigm becoming mainstream, the high storage and deployment costs of individual finetuned models on multiple tasks pose critical challenges. Delta compression attempts to lower the costs by reducing the redundancy of delta parameters (i.e., the difference between the finetuned and pre-trained model weights). However, existing methods usua...
Recent advancements in 3D reconstruction coupled with neural rendering techniques have greatly improved the creation of photo-realistic 3D scenes, i...
Amodal instance segmentation, which aims to detect and segment both visible and invisible parts of objects in images, plays a crucial role in variou...
Recently, 3D Gaussian Splatting (3D-GS) has emerged, showing real-time rendering speeds and high-quality results in static scenes. Although 3D-GS sh...
Segment anything model (SAM) has shown impressive general-purpose segmentation performance on natural images, but its performance on camouflaged obj...
Image segmentation is a challenging task influenced by multiple sources of uncertainty, such as the data labeling process or the sampling of trainin...
The Segment Anything Model (SAM) has revolutionized open-set interactive image segmentation, inspiring numerous adapters for the medical domain. How...
Tumor segmentation plays a critical role in histopathology, but it requires costly, fine-grained image-mask pairs annotated by pathologists. Thus, s...
The reliance on large labeled datasets presents a significant challenge in medical image segmentation. Few-shot learning offers a potential solution...
Autonomous navigation is a fundamental task for robot vacuum cleaners in indoor environments. Since their core function is to clean entire areas, ro...
Rapid advancement of sequencing technologies now allows for the utilization of precise signals at single-cell resolution in various omics studies. How...
Accurate prediction of pathogenic variants in human disease-associated genes would have a profound effect on clinical decision-making; however, it rem...
In post-genome-wide association study era, interpretation of noncoding variants remains a significant challenge due to their complexity and the limite...
BACKGROUND: Multianalyte machine learning (ML) models can potentially identify previously undetectable wrong blood in tube (WBIT) errors, improving up...
Machine learning applications in protein sciences have ushered in a new era for designing molecules in silico. Antibodies, which currently form the ...
With growing demand in media and social networks for personalized images, the need for advanced head-swapping techniques, integrating an entire head...
The development of therapeutic antibodies heavily relies on accurate predictions of how antigens will interact with antibodies. Existing computation...
The organization of subcellular components in a cell is critical for its function and studying cellular processes, protein-protein interactions, ident...
Breast cancer affects millions globally, necessitating precise biomarker testing for effective treatment. HER2 testing is crucial for guiding therapy,...