Fine-grained talking face generation with video reinterpretation
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The attention mechanism has gained wide adoption in computer vision due to its spectral capabilities for multimodal applications such as video description and salient object detection.
“Since its introduction, the AM has gained wide adoption in the computer vision community due to its spectral capabilities for many multimodal applications such as video description [233, 234], salient object detection [235], etc.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [234]
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References 50 · Cited by 11
- Authors
- Huang, X., Wang, M., & Gong, M.
- Affiliations
- Memorial University of Newfoundland (CA)
- University of Guelph (CA)
- Corresponding author
- Huang, Xin
- Venue
- The Visual Computer
- Published
- 2020-09-22
- Volume
- 37
- Issue
- 1
- Pages
- 95-105
- ISSN
- 0178-2789 (ISSN-L)
- 1432-2315
- OpenAlex
- W3088751268
- Cited by
- 11
- References
- 50
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 2 · 2022: 3 · 2021: 3
- FWCI
- 0.78
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Visual Computer (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- talking face generation, audio-video synchronization, adversarial learning
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.
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