The LIV Golf United Kingdom field is headlined by the likes of Jon Rahm, Cameron Smith, Tyrrell Hatton, Brooks Koepka, Bryson DeChambeau, Anthony Kim and more. This is set to be a 54-player field is ...

Rhodes College economics professors Marshall Gramm and Nick McKinney published in these pages a study showing the estimated ...

Automatic Lung Segmentation in Chest X-Ray Images Using SAM With Prompts From YOLO Abstract: Despite the impressive performance of current deep learning models in the field of medical imaging, ...

Current AI-assisted skin image diagnosis has achieved dermatologist-level performance in classifying skin cancer, driven by rapid advancements in deep learning architectures. However, unlike ...

However, directly deploying SAM on weakly supervised object detection task meets with two issues. Firstly, SAM needs meticulously-designed prompts, and such expert-level prompts restrict their ...

The recent emergence of the Segment Anything Model (SAM) enables various domain-specific segmentation tasks to be tackled cost-effectively by using bounding boxes as prompts. However, in scene text ...

We have witnessed remarkable progress in foundation models in vision tasks. Currently, several recent works have utilized the segmenting anything model (SAM) to boost the segmentation performance in ...

Quantitative evaluation of skin wounds is important in tracking the healing process and planning the treatment. For non-invasive and automatic analysis of the wound, segmentation of the wound area ...

Recent advancements in foundation models, such as the Segment Anything Model (SAM), have shown strong performance in various vision tasks, particularly image segmentation, due to their impressive zero ...

The Segment Anything Model (SAM) has recently demonstrated significant potential in medical image segmentation. Although SAM is primarily trained on 2D images, attempts have been made to apply it to ...

The Segment Anything Model (SAM) excels at generating high-quality object masks with various prompts but struggles in ultra-low light. We developed EP-SAM (Edge-Detection Prompt SAM) with a Low Light ...