Papers › Unified Perceptual Parsing for Scene Understanding
Unified Perceptual Parsing for Scene Understanding
Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, Jian Sun
Humans recognize the visual world at multiple levels: we effortlessly categorize scenes and detect objects inside, while also identifying the textures and surfaces of the objects along with their different compositional parts. In this paper, we study a new task called Unified Perceptual Parsing, which requires the machine vision systems to recognize as many visual concepts as possible from a given image. A multi-task framework called UPerNet and a training strategy are developed to learn from heterogeneous image annotations. We benchmark our framework on Unified Perceptual Parsing and show that it is able to effectively segment a wide range of concepts from images. The trained networks are further applied to discover visual knowledge in natural scenes. Models are available at \url{https://github.com/CSAILVision/unifiedparsing}.
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Code
Syntology Ran 9 of 28 code samples harvested from 4 repositories linked to this paper; 19 have no recorded run. Of those that ran: 1 ran · honoured contract; 6 ran · our draft was wrong; 1 ran · fixture could not drive it; 1 ran with no contract checked.
By repository: community (archive-listed): 28 samples from 4 repositories, 9 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.
25 repositories listed; official and paper-mentioned ones first.
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
28 samples harvested; 9 ran; 1 honoured the contract we drafted; 19 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 0 of the 28 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
Harvested from 4 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 2D Semantic Segmentation | WildScenes | UPerNet (ConvNeXt-L) | mIoU | 47.30 | #2 of 5 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | UperNet (ResNet-101) | Validation mIoU | 42.66 | #215 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K val | UperNet (ResNet-101) | mIoU | 42.66 | #93 of 95 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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