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Out of Time: Automated Lip Sync in the Wild
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“contrastive loss has been shown [66], [28] to be a powerful objective for similar tasks.”
From Do You Really Mean That? Content Driven Audio-Visual Deepfake Dataset and Multimodal Method for Temporal Forgery Localization (2022) · cites this paper as [66]
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Metadata
- Authors
- Chung, J. S., & Zisserman, A.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Chung, Joon Son
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 251-263
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2604379605
- Cited by
- 689
- References
- 30
- Citations by year
- 2026: 79 · 2025: 102 · 2024: 135 · 2023: 96 · 2022: 76 · 2021: 80 · 2020: 51 · 2019: 31 · 2018: 27 · 2017: 10
- FWCI
- 23.11
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/M013774/1)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Engineering › Media Technology › Multimedia Communication and Technology
- Keywords
- lip synchronization, speech, two-stream convolutional neural network, unsupervised learning, active speaker detection, lip reading
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.
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