# From SAM to SAM 2: Exploring Improvements in Meta's Segment Anything Model

arXiv: https://arxiv.org/abs/2408.06305 · HTML: https://mlsanity.com/paper/2408.06305/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 12 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Rotary Position Embedding for Vision Transformer](https://mlsanity.com/paper/2403.13298/) (2024)
- [Window Attention is Bugged: How not to Interpolate Position Embeddings](https://mlsanity.com/paper/2311.05613/) (2023)

## Built on by (most cited first, 3 of 3)

- [SAM2 for Image and Video Segmentation: A Comprehensive Survey](https://mlsanity.com/paper/2503.12781/) (2025)
- [BYOCL: Build Your Own Consistent Latent with Hierarchical Representative Latent Clustering](https://mlsanity.com/paper/2410.15060/) (2024)
- [When Tracking Fails: Analyzing Failure Modes of SAM2 for Point-Based Tracking in Surgical Videos](https://mlsanity.com/paper/2510.02100/) (2025)

## 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.