Optical Flow Estimation with Deep Learning, a Survey on Recent Advances
Integrity signals
Registry
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
For many vision-based multimodal problems, fusing optical flow and depth information with RGB has been shown to yield the best performance.
“The emphasis on these modalities (RGB, depth, and flow data) is based on the fact that for many vision-based multimodal problems, it has been shown that the fusion of optical flow and depth information with RGB yields the best performance [242, 243].”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [243]
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References 130 · Cited by 29
- Authors
- Savian, S., Elahi, M., & Tillo, T.
- Affiliations
- Free University of Bozen-Bolzano (IT)
- Corresponding author
- Savian, Stefano
- Venue
- Unsupervised and Semi-Supervised Learning
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 257-287
- ISSN
- 2522-848X (ISSN-L)
- 2522-8498
- OpenAlex
- W3003852583
- Cited by
- 29
- References
- 130
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 5 · 2023: 7 · 2022: 7 · 2021: 2 · 2020: 4
- FWCI
- 9.55
- Type
- book chapter
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Unsupervised and semi-supervised learning (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- optical flow, deep learning, biometrics, motion analysis, object tracking, gait recognition, motion detection, video compression
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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