From SAM to SAM 2: Exploring Improvements in Meta's Segment Anything Model
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Abstract (arXiv)
The Segment Anything Model (SAM), introduced to the computer vision community by Meta in April 2023, is a groundbreaking tool that allows automated segmentation of objects in images based on prompts such as text, clicks, or bounding boxes. SAM excels in zero-shot performance, segmenting unseen objects without additional training, stimulated by a large dataset of over one billion image masks. SAM 2 expands this functionality to video, leveraging memory from preceding and subsequent frames to generate accurate segmentation across entire videos, enabling near real-time performance. This comparison shows how SAM has evolved to meet the growing need for precise and efficient segmentation in various applications. The study suggests that future advancements in models like SAM will be crucial for improving computer vision technology.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 7 of its 21 references that are in the map. First posted on arXiv 12 Aug 2024.
Builds on
7 of 21 references.
References · 7
- 2017Attention Is All You Needcited by 59,932
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2024SAM 2: Segment Anything in Images and Videoscited by 2,784
- 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,170
- 2024Rotary Position Embedding for Vision Transformercited by 226
- 2023Window Attention is Bugged: How not to Interpolate Position Embeddingscited by 18
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Citing papers per year: 2024: 1; 2025: 2.
3 papers.