SAM 2: Segment Anything in Images and Videos
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Cited by 215 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
We present Segment Anything Model 2 (SAM 2), a foundation model towards solving promptable visual segmentation in images and videos. We build a data engine, which improves model and data via user interaction, to collect the largest video segmentation dataset to date. Our model is a simple transformer architecture with streaming memory for real-time video processing. SAM 2 trained on our data provides strong performance across a wide range of tasks. In video segmentation, we observe better accuracy, using 3x fewer interactions than prior approaches. In image segmentation, our model is more accurate and 6x faster than the Segment Anything Model (SAM). We believe that our data, model, and insights will serve as a significant milestone for video segmentation and related perception tasks. We are releasing our main model, dataset, as well as code for model training and our demo.
Key facts. Cited by 2,863 papers in the map (data through 14 Oct 2026). Builds on 92 of its 126 references that are in the map. First posted on arXiv 1 Aug 2024.
Builds on
92 of 126 references.
References · 92
- 2023Segment Anythingcited by 8,234
- 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
- 2016Feature Pyramid Networks for Object Detectioncited by 5,036
- 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,703
- 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
- 2023FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioningcited by 2,598
- 2019LVIS: A Dataset for Large Vocabulary Instance Segmentationcited by 1,217
- 2018Model Cards for Model Reportingcited by 1,080
- 2016Semantic Understanding of Scenes through the ADE20K Datasetcited by 1,069
- 2021Scaling Vision Transformerscited by 999
- 2017The 2017 DAVIS Challenge on Video Object Segmentationcited by 957
38 more
- 2022Exploring Plain Vision Transformer Backbones for Object Detectioncited by 724
- 2020Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understandingcited by 604
- 2021Carbon Emissions and Large Neural Network Trainingcited by 567
- 2020Rescaling Egocentric Visioncited by 540
- 2019Quantifying the Carbon Emissions of Machine Learningcited by 521
- 2021TrackFormer: Multi-Object Tracking with Transformerscited by 518
- 2023Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentationcited by 490
- 2023Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectivescited by 490
- 2022XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Modelcited by 439
- 2019Video Object Segmentation using Space-Time Memory Networkscited by 438
- 2018YouTube-VOS: A Large-Scale Video Object Segmentation Benchmarkcited by 383
- 2023Segment Anything in High Qualitycited by 376
- 2020Virtual KITTI 2cited by 353
- 2023Segment Anything Model for Medical Image Analysis: an Experimental Studycited by 291
- 2023Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistlescited by 290
- 2024Rotary Position Embedding for Vision Transformercited by 236
- 2016One-Shot Video Object Segmentationcited by 235
- 2023MOSE: A New Dataset for Video Object Segmentation in Complex Scenescited by 234
- 2023Track Anything: Segment Anything Meets Videoscited by 234
- 2018YouTube-VOS: Sequence-to-Sequence Video Object Segmentationcited by 232
- 2021Associating Objects with Transformers for Video Object Segmentationcited by 226
- 2021Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentationcited by 203
- 2023Tracking Anything with Decoupled Video Segmentationcited by 197
- 2023Segment and Track Anythingcited by 193
- 2023Putting the Object Back into Video Object Segmentationcited by 188
- 2023RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Modelcited by 181
- 2021Reviving Iterative Training with Mask Guidance for Interactive Segmentationcited by 163
- 2023Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imagingcited by 161
- 2021Occluded Video Instance Segmentation: A Benchmarkcited by 154
- 2016Learning Video Object Segmentation from Static Imagescited by 151
- 2022Decoupling Features in Hierarchical Propagation for Video Object Segmentationcited by 148
- 2019WoodScape: A multi-task, multi-camera fisheye dataset for autonomous drivingcited by 144
- 2020Collaborative Video Object Segmentation by Foreground-Background Integrationcited by 142
- 2020Video Panoptic Segmentationcited by 142
- 2022EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object Relationscited by 140
- 2021Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentationcited by 133
- 2021Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusioncited by 129
- 2017Online Adaptation of Convolutional Neural Networks for Video Object Segmentationcited by 109
50 most cited of 92 All 92 (JSON)
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Citing papers per year: 2024: 254; 2025: 1305; 2026: 1304.
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