Recent advancements in biomedical image analysis have been significantly driven by the Segment Anything Model (SAM). This transformative technology, originally developed for …
Abstract The Segment Anything Model (SAM) has garnered significant attention for its versatile segmentation abilities and intuitive prompt-based interface. However its application …
Y Zhang, S Hu, C Jiang, Y Cheng, Y Qi - arXiv preprint arXiv:2311.10529, 2023 - arxiv.org
The introduction of the Segment Anything Model (SAM) has marked a significant advancement in prompt-driven image segmentation. However, SAM's application to medical …
W He, Y Zhang, W Zhuo, L Shen… - Proceedings of the …, 2024 - openaccess.thecvf.com
Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a few labeled samples. Current FSS methods are commonly built on the assumption that their …
Vision Transformers (ViTs) have become prominent models for solving various vision tasks. However, the interpretability of ViTs has not kept pace with their promising performance …
The Segment Anything Model (SAM) has shown impressive performance when applied to natural image segmentation. However, it struggles with geographical images like aerial and …
Precise, and automated segmentation of construction and demolition waste (CDW) is crucial for recognizing the composition of mixed waste streams and facilitating automatic waste …
Segment Anything Model (SAM) demonstrated impressive performance in zero-shot promptable segmentation on natural images. The recently released Segment Anything …
The recent wave of foundation models has witnessed tremendous success in computer vision (CV) and beyond, with the segment anything model (SAM) having sparked a passion …