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Translate the following blog post title into English, concise and natural. Return plain text only without quotes. SAM 3：为现代视觉工作流程引入更强大的分割架构

Honghao Wang

— 2 min read

Meta Releases SAM 3 — Major Update to Segment Anything Model

Meta has launched SAM 3, the latest and most significant update to its Segment Anything Model since the initial release.

Designed for more stable and context-aware segmentation, SAM 3 delivers notable improvements in:

  • Accuracy
  • Boundary quality
  • Robustness in real-world scenarios

The goal: Make segmentation more reliable for both research and production systems.

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Key Improvements in SAM 3

1. Redesigned Architecture

  • Handles fine structures, overlapping objects, and ambiguous regions more effectively
  • Provides consistent masks for small objects and cluttered environments
  • Revised training dataset improves coverage and reduces failures in:
  • Unusual lighting conditions
  • Occlusions

2. Performance Enhancements

  • Faster inference on GPUs and mobile-class hardware
  • Reduced latency for:
  • Interactive use
  • Batch processing
  • Ships with optimized runtimes for:
  • PyTorch
  • ONNX
  • Web execution
  • Integrations designed to simplify deployment — minimal workflow changes needed

3. Improved Contextual Understanding

  • Interprets relationships between objects, not just boundaries
  • Produces segmentation that aligns closer with human perception
  • Benefits downstream tasks requiring cleaner or semantically meaningful masks

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Broader Vision

Meta’s research team aims to position SAM 3 as a general-purpose component within multimodal systems.

Segmentation is increasingly treated as an infrastructural capability, rather than a specialized module.

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Community Reaction

Reactions have been mixed but pragmatic:

  • One Reddit user commented:
  • > It seems like a software update, not a new model.
  • Another pointed out:
  • > Text prompting in SAM2 was very experimental, and the public model didn't support it. Now the public model seems to have it, which is a pretty big step for a lot of practitioners.

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Applications

Beyond interactive segmentation, SAM 3 supports diverse use cases:

  • AR/VR scene understanding
  • Scientific imaging
  • Video editing
  • Automated labeling
  • Robotics perception

Meta emphasizes that SAM 3 is built to fit existing vision pipelines naturally — no need for dedicated infrastructure or task-specific training.

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Availability

SAM 3 is available open-source, including:

  • Model weights
  • Documentation
  • Deployment examples

This release combines a more advanced architecture with broad platform compatibility, reinforcing SAM’s role as a general-purpose segmentation tool for both research and industry.

📄 Technical details in the official paper.

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Would you like me to also prepare a quick comparison table between SAM 2 and SAM 3 for clarity? That would make the update easier to digest for readers.

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Translate the following blog post title into English, concise and natural. Return plain text only without quotes. 哈佛大学 R 编程课程介绍

Harvard CS50: Introduction to Programming with R Harvard University offers exceptional beginner-friendly computer science courses. We’re excited to announce the release of Harvard CS50’s Introduction to Programming in R, a powerful language widely used for statistical computing, data science, and graphics. This course was developed by Carter Zenke.