Papers › Per-Pixel Classification is Not All You Need for Semantic Segmentation

Per-Pixel Classification is Not All You Need for Semantic Segmentation

13 Jul 2021NeurIPS 2021 12arXiv:2107.06278archive 2025-07-28

Bowen Cheng, Alexander G. Schwing, Alexander Kirillov

Modern approaches typically formulate semantic segmentation as a per-pixel classification task, while instance-level segmentation is handled with an alternative mask classification. Our key insight: mask classification is sufficiently general to solve both semantic- and instance-level segmentation tasks in a unified manner using the exact same model, loss, and training procedure. Following this observation, we propose MaskFormer, a simple mask classification model which predicts a set of binary masks, each associated with a single global class label prediction. Overall, the proposed mask classification-based method simplifies the landscape of effective approaches to semantic and panoptic segmentation tasks and shows excellent empirical results. In particular, we observe that MaskFormer outperforms per-pixel classification baselines when the number of classes is large. Our mask classification-based method outperforms both current state-of-the-art semantic (55.6 mIoU on ADE20K) and panoptic segmentation (52.7 PQ on COCO) models.

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Code

facebookresearch/MaskFormer officialmentioned on GitHubpytorchNOASSERTION report
huggingface/transformers mentioned on GitHubpytorch report
open-mmlab/mmdetection pytorchApache-2.0 report

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Tasks

AllClassificationPanoptic SegmentationSegmentationSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Panoptic Segmentation ADE20K val MaskFormer (R101 + 6 Enc) PQ 35.7 #24 of 25 Archive leaderboard report
Panoptic Segmentation COCO minival MaskFormer (single-scale) PQ 52.7 #21 of 31 Archive leaderboard report
Panoptic Segmentation COCO minival MaskFormer (single-scale) PQst 44.0 #21 of 31 Archive leaderboard report
Panoptic Segmentation COCO minival MaskFormer (single-scale) PQth 58.5 #21 of 31 Archive leaderboard report
Panoptic Segmentation COCO minival MaskFormer (single-scale) RQ 63.5 #21 of 31 Archive leaderboard report
Panoptic Segmentation COCO minival MaskFormer (single-scale) SQ 81.8 #21 of 31 Archive leaderboard report
Panoptic Segmentation COCO test-dev MaskFormer (Swin-L) PQ 53.3 #9 of 38 Archive leaderboard report
Panoptic Segmentation COCO test-dev MaskFormer (Swin-L) PQst 44.5 #9 of 38 Archive leaderboard report
Panoptic Segmentation COCO test-dev MaskFormer (Swin-L) PQth 59.1 #9 of 38 Archive leaderboard report
Semantic Segmentation ADE20K MaskFormer(Swin-B) Validation mIoU 53.8 #73 of 235 Archive leaderboard report
Semantic Segmentation ADE20K MaskFormer(ResNet-101) Validation mIoU 48.1 #153 of 235 Archive leaderboard report
Semantic Segmentation ADE20K val MaskFormer (Swin-L, ImageNet-22k pretrain) mIoU 55.6 #28 of 95 Archive leaderboard report
Semantic Segmentation Mapillary val MaskFormer (ResNet-50) mIoU 55.4 #4 of 8 Archive leaderboard report

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