摘要:近年来,基础模型在计算机视觉(CV)及其他领域取得了巨大的成功,其中 Segment Anything 模型(SAM)激发了探索任务无关视觉基础模型的热情。得益于其卓越的无监督泛化能力,SAM 目前正在挑战 CV 领域的众多传统范式,不仅在各种图像分割和多模态分割(例如文本到掩码)任务中表现出色,而且在视频领域也取得了非凡的成绩。...
https://ai.meta.com/blog/segment-anything-2/, 视频播放量 4、弹幕量 0、点赞数 0、投硬币枚数 0、收藏人数 0、转发人数 0, 视频作者 悠VS尚, 作者简介 ,相关视频:y2mate.com - Meta Unveils SAM 2 A Unified Model for RealTime Object Segmentation,2千不到入手meta
我们介绍了Segment Anything Model 2(SAM 2),这是一个统一的视频和图像分割模型(我们将图像视为单帧视频)。我们的工作包括任务、模型和数据集(见图1)。我们专注于可提示的视觉分割(Promptable Visual Segmentation,PVS)任务,它将图像分割推广到视频领域。该任务接受视频任意帧上的点、框或掩模作为输入,以定义一个...
We propose a novel model (VP-SAM) adapted from segment anything model (SAM) for video polyp segmentation (VPS), which isa challenging task due to (1) the low contrast between polypsand background and (2) the large frame-to-frame variations ofpolyp size, position, and shape. Our aim is...
因此，我们提出了Track Anything Model（TAM），旨在实现高性能的视频对象跟踪与分割。TAM结合了XMEM与SAM，仅需少量人类参与，便能在一次推理中跟踪视频中的任意对象，并提供满意结果。无需额外训练，其交互式设计在视频对象跟踪与分割方面表现出色。所有资源可在github.com/gaomingqi/TrackAnything获得。我们...
title={Segment Anything for Videos: A Systematic Survey}, author={Chunhui Zhang, Yawen Cui, Weilin Lin, Guanjie Huang, Yan Rong, Li Liu, Shiguang Shan}, journal={arXiv}, year={2024} } Survey The first comprehensive SAM survey:Chunhui Zhang, Li Liu, Yawen Cui, Guanjie Huang, Weilin Lin...
Recent advances in segmentation foundation models have enabled accurate and efficient segmentation across a wide range of natural images and videos, but their utility to medical data remains unclear. In this work, we first present a comprehensive benchmarking of the Segment Anything Model 2 (SAM2)...
Segment Anything Model 2 (SAM 2)is a foundation model towards solving promptable visual segmentation in images and videos. We extend SAM to video by considering images as a video with a single frame. The model design is a simple transformer architecture with streaming memory for real-time video...
This repository is for the first comprehensive survey on Meta AI's Segment Anything Model (SAM). - GitHub - jinx2018/Awesome-Segment-Anything: This repository is for the first comprehensive survey on Meta AI's Segment Anything Model (SAM).
1、 Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic Segmentation Tianle Chen, Zheda Mai, Ruiwen Li, Wei-lun Chao 图像级监督的弱监督语义分割(WSSS)由于其标注成本较像素级标注低而受到越来越多的关注。大多数现有方法依赖于类激活图(Class Activation Maps, CAM)来生成像...