编辑丨 机器之心 点击下方 卡片 ，关注" 具身智能之心 "公众号 >> 点击进入→ 具身 智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 说出概念，SAM 3 就明白你在说什么，并在所有出现的位置精确描绘出边界。 Meta 的「分割一切」再上新？ 9 月 12 日，一篇匿名论文「SAM 3: SEGMENT ANYTHING WITH CONCEPTS」登陆 ICLR 2026，引发网友广泛关注。 大家纷纷猜测，这篇论文出自 Meta，毕竟文风和 Meta 以前发布的论文非常相似。再加上 SAM 与 SAM 2 均由 Meta 推出，这让外界几乎可以确定，SAM 3 就是 Meta「Segment Anything」系列的正式续作。 即将文本和 / 或图像范例作为输入，为每一个与该概念匹配的对象预测实例掩码和语义掩码，同时在视频帧之间保持对象身份的一致性。该工作的重点是识别原子 视觉概念 (atomic visual concepts)，因此 将输入文本限制为简单的名词短语，例如「红苹果」或「条纹猫」，只要描述你想要的东西 ...

Core Insights - The article discusses the release of a new paper titled "SAM 3: Segment Anything with Concepts," which is believed to be a continuation of Meta's "Segment Anything" series, following SAM 1 and SAM 2 [1][3][4]. Group 1: Overview of SAM 3 - SAM 3 introduces a new task called Promptable Concept Segmentation (PCS), allowing users to input text or image examples to predict instance and semantic masks for matching objects while maintaining identity consistency across video frames [8][12]. - The model focuses on identifying atomic visual concepts, enabling it to understand simple noun phrases like "red apple" or "striped cat" for segmentation tasks [8][12]. - SAM 3 improves upon its predecessors by enhancing performance in promptable visual segmentation and establishing new standards for PCS [18]. Group 2: Performance Metrics - SAM 3 shows significant performance improvements, achieving at least a 2x enhancement on the newly proposed SA-Co benchmark compared to previous systems [13]. - In the LVIS dataset, SAM 3 achieved a zero-shot mask average precision of 47.0, surpassing the previous best of 38.5 [13]. - The model processes images with over 100 objects in just 30 milliseconds on a single H200 GPU [14]. Group 3: Methodology and Data - SAM 3 employs a dual encoder-decoder transformer architecture, integrating a detector with a tracker and memory module for video applications [20]. - The research developed a scalable human-machine collaborative data engine, annotating a high-quality training dataset with 4 million unique phrases and 520 million masks [21]. - The PCS benchmark includes 124K images and 1.7K videos with 214K unique concepts, significantly expanding the concept count compared to existing benchmarks [25]. Group 4: Comparative Analysis - SAM 3 outperforms previous models in various tasks, including instance segmentation, box detection, and semantic segmentation across multiple datasets [27][28]. - In open vocabulary semantic segmentation experiments, SAM 3 exceeded the performance of strong baseline models [29]. - The model also demonstrated superior object counting accuracy and segmentation capabilities compared to other models [33].