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

• URL: http://arxiv.org/abs/2107.06278v1
• Date: Tue, 13 Jul 2021 17:59:50 GMT
• Title: Per-Pixel Classification is Not All You Need for Semantic Segmentation
• Authors: Bowen Cheng and Alexander G. Schwing and Alexander Kirillov
• Abstract summary: Mask classification is sufficiently general to solve both semantic- and instance-level segmentation tasks. We propose MaskFormer, a simple mask classification model which predicts a set of binary masks. Our method outperforms both current state-of-the-art semantic (55.6 mIoU on ADE20K) and panoptic segmentation (52.7 PQ on COCO) models.
• Score: 184.2905747595058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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