mlsanityData to 14 Oct 2026

SiamMask: A Framework for Fast Online Object Tracking and Segmentation

arXiv 2207.02088 ↗First posted 5 Jul 2022cs.CVcited by 13

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Abstract (arXiv)

In this paper we introduce SiamMask, a framework to perform both visual object tracking and video object segmentation, in real-time, with the same simple method. We improve the offline training procedure of popular fully-convolutional Siamese approaches by augmenting their losses with a binary segmentation task. Once the offline training is completed, SiamMask only requires a single bounding box for initialization and can simultaneously carry out visual object tracking and segmentation at high frame-rates. Moreover, we show that it is possible to extend the framework to handle multiple object tracking and segmentation by simply re-using the multi-task model in a cascaded fashion. Experimental results show that our approach has high processing efficiency, at around 55 frames per second. It yields real-time state-of-the-art results on visual-object tracking benchmarks, while at the same time demonstrating competitive performance at a high speed for video object segmentation benchmarks.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 73 of its 110 references that are in the map. First posted on arXiv 5 Jul 2022.

Builds on

73 of 110 references.

References · 73

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38 more
  1. 2018TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wildcited by 343
  2. 2018ATOM: Accurate Tracking by Overlap Maximizationcited by 285
  3. 2018Fast Online Object Tracking and Segmentation: A Unifying Approachcited by 272
  4. 2016ECO: Efficient Convolution Operators for Trackingcited by 239
  5. 2016One-Shot Video Object Segmentationcited by 235
  6. 2018YouTube-VOS: Sequence-to-Sequence Video Object Segmentationcited by 232
  7. 2015Learning Multi-Domain Convolutional Neural Networks for Visual Trackingcited by 209
  8. 2020Siamese Box Adaptive Network for Visual Trackingcited by 189
  9. 2018Distractor-aware Siamese Networks for Visual Object Trackingcited by 187
  10. 2019SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelinescited by 177
  11. 2016Learning Video Object Segmentation from Static Imagescited by 151
  12. 2019SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Trackingcited by 148
  13. 2017Detect to Track and Track to Detectcited by 131
  14. 2015A Novel Performance Evaluation Methodology for Single-Target Trackerscited by 113
  15. 2017YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Videocited by 109
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  17. 2018Efficient Video Object Segmentation via Network Modulationcited by 98
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  19. 2017Video Object Segmentation Without Temporal Informationcited by 93
  20. 2017SegFlow: Joint Learning for Video Object Segmentation and Optical Flowcited by 92
  21. 2017End-to-end representation learning for Correlation Filter based trackingcited by 90
  22. 2018VideoMatch: Matching based Video Object Segmentationcited by 90
  23. 2016Learning to Track at 100 FPS with Deep Regression Networkscited by 89
  24. 2021LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Searchcited by 82
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  28. 2020Graph Attention Trackingcited by 70
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  30. 2016Learning to Refine Object Segmentscited by 67
  31. 2016Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Trackingcited by 66
  32. 2016Learning Spatially Regularized Correlation Filters for Visual Trackingcited by 66
  33. 2020Efficient Adversarial Attacks for Visual Object Trackingcited by 66
  34. 2018Siamese Cascaded Region Proposal Networks for Real-Time Visual Trackingcited by 65
  35. 2018Learning Spatial-Temporal Regularized Correlation Filters for Visual Trackingcited by 63
  36. 2018A Twofold Siamese Network for Real-Time Object Trackingcited by 62
  37. 2016Siamese Instance Search for Trackingcited by 60
  38. 2018Learning Dynamic Memory Networks for Object Trackingcited by 59

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Citing papers per year: 2022: 1; 2023: 3; 2024: 4; 2025: 4; 2026: 1.

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