15 datasets found

The dataset used for medical image segmentation.

Three publicly available medical imaging datasets: Breast Ultrasound Scan Images (BUSI), CVC-ClinicDB, and ISIC-2016.

Open World Object Detection (OWOD) is a novel and challenging computer vision task that enables object detection with the ability to detect unknown objects.

EdgeSAM is an accelerated variant of the Segment Anything Model (SAM) optimized for efficient execution on edge devices with minimal compromise in performance.

This work investigates the robustness of SAM to corruptions and adversarial attacks.

SAM is a foundation model in computer vision that can segment anything.

This work conducts the first yet comprehensive study on TAA on SAM in a black-box setup, assuming no access to prompt and model.

The dataset used in this study for evaluating the performance of the Segment Anything Model (SAM) in clinical radiotherapy.

The COSMOS 553K dataset is a large-scale medical image segmentation dataset.

The proposed processing pipeline encom- passes two fundamental parts: regions of interest mask generation, followed by the application of hyperspectral data processing...

The Segment Anything Model (SAM) has been proven to be a powerful foundation model for image segmentation tasks, which is an important task in computer vision. However, the...

Unsupervised video object segmentation has made significant progress in recent years, but the manual annotation of video mask datasets is expensive and limits the diversity of...

The dataset used in the paper is a collection of tasks from DMControl and Adroit, with 8 tasks from DMControl and 3 tasks from Adroit.

The dataset used in the paper is not explicitly described, but it is mentioned that it is a large-scale medical image segmentation dataset.

The dataset used in this paper is the Meta Research's Segment Anything Model (SAM) dataset, which consists of images.