Semantic Understanding of Scenes Through the ADE20K Dataset
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Existing segmentation datasets can hardly support LUSS due to the large image complexity but small data scale.
“Existing segmentation datasets can hardly support LUSS due to the large image complexity but small data scale.”
From Large-Scale Unsupervised Semantic Segmentation (2023) · cites this paper as [8]
The Introduction (p. 2) describes limitations in existing segmentation datasets’ object and scene coverage, and Table 1 (p. 8) compares their image counts and objects per image. These passages support concerns about dataset coverage and scale, but the paper does not assess whether those datasets can support LUSS or attribute any such difficulty to the combination of image complexity and small data scale. (LLM, conf=0.80)
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“In a second experiment, we trained the end- to-end model with a more challenging dataset containing complex images of naturalistic scenes (the ADE20K dataset Zhou et al., 2019 ).”
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- Authors
- Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A., & Torralba, A.
- Affiliations
- Chinese University of Hong Kong (HK)
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Peking University (CN)
- University of Toronto (CA)
- Corresponding author
- Zhou, Bolei
- Venue
- International Journal of Computer Vision
- Published
- 2018-12-07
- Volume
- 127
- Issue
- 3
- Pages
- 302-321
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2507296351
- Cited by
- 1,831
- References
- 43
- Citations by year
- 2026: 211 · 2025: 295 · 2024: 354 · 2023: 295 · 2022: 199 · 2021: 167 · 2020: 134 · 2019: 94 · 2018: 39 · 2017: 35 · 2016: 8
- FWCI
- 54.27
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Computer Vision (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1524817)
- Samsung
- Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Keywords
- ADE20K, scene understanding, semantic segmentation, instance segmentation, scene parsing, pixel-wise annotation, everyday scenes, object parts, benchmark dataset, batch size
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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