mlsanityData to 7 Oct 2026

Pyramid Scene Parsing Network

arXiv 1612.01105 ↗First posted 4 Dec 2016cs.CVcited by 2,472

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Cited by 23 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Scene parsing is challenging for unrestricted open vocabulary and diverse scenes. In this paper, we exploit the capability of global context information by different-region-based context aggregation through our pyramid pooling module together with the proposed pyramid scene parsing network (PSPNet). Our global prior representation is effective to produce good quality results on the scene parsing task, while PSPNet provides a superior framework for pixel-level prediction tasks. The proposed approach achieves state-of-the-art performance on various datasets. It came first in ImageNet scene parsing challenge 2016, PASCAL VOC 2012 benchmark and Cityscapes benchmark. A single PSPNet yields new record of mIoU accuracy 85.4% on PASCAL VOC 2012 and accuracy 80.2% on Cityscapes.

Key facts. Cited by 2,472 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Dec 2016.

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Citing papers per year: 2021: 623; 2022: 547; 2023: 525; 2024: 379; 2025: 267; 2026: 131.

2,472 papers.

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  2. 2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,338
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  12. 2021DenseCLIP: Language-Guided Dense Prediction with Context-Aware Promptingcited by 530
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