Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning
Open accessAbstract
This paper tackles the problem of video object segmentation, given some user annotation which indicates the object of interest. The problem is formulated as pixel-wise retrieval in a learned embedding space: we embed pixels of the same object instance into the vicinity of each other, using a fully convolutional network trained by a modified triplet loss as the embedding model. Then the annotated pixels are set as reference and the rest of the pixels are classified using a nearest-neighbor approach. The proposed method supports different kinds of user input such as segmentation mask in the first frame (semi-supervised scenario), or a sparse set of clicked points (interactive scenario). In the semi-supervised scenario, we achieve results competitive with the state of the art but at a fraction of computation cost (275 milliseconds per frame). In the interactive scenario where the user is able to refine their input iteratively, the proposed method provides instant response to each input, and reaches comparable quality to competing methods with much less interaction.
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Adopting a memory architecture to store object information from past frames is indispensable for accurately tracking objects in video.
“To address the above challenges, adopting a memory architecture to store the object information from past frames is indispensable for accurately tracking objects in video [5,7,10,29,40,52,56,57,64,66,68].”
From SAM2LONG: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree (2025) · cites this paper as [10]
The 'Retrieval with Online Adaptation' section says the method adds high-confidence samples from later frames to a pool of reference samples. The ablation analysis reports a slight accuracy gain from online adaptation (+1.2% in J), but does not establish that a memory architecture is indispensable for accurate video object tracking. Page numbers are unavailable in our copy. (LLM, conf=0.87)
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- Authors
- Chen, Y., Pont-Tuset, J., Montes, A., & Gool, L. V.
- Affiliations
- ETH Zurich (CH)
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 1189-1198
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2964157492
- Cited by
- 273
- References
- 48
- Citations by year
- 2026: 3 · 2025: 12 · 2024: 25 · 2023: 36 · 2022: 43 · 2021: 48 · 2020: 59 · 2019: 43 · 2018: 4
- FWCI
- 11.60
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- video object segmentation, triplet loss, pixel embedding, nearest neighbor classification, semi-supervised segmentation, interactive segmentation, user annotation, computational efficiency
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