Masked-attention Mask Transformer for Universal Image Segmentation

Image segmentation groups pixels with different semantics, e.g., category or instance membership. Each choice of semantics defines a task. While only the semantics of each task differ, current research focuses on designing spe-cialized architectures for each task. We present Masked- attention Mask T...

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Bibliographic Details
Published in:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) pp. 1280 - 1289
Main Authors: Cheng, Bowen, Misra, Ishan, Schwing, Alexander G., Kirillov, Alexander, Girdhar, Rohit
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2022
Subjects:
categorization
Computational modeling
Computer architecture
Feature extraction
grouping and shape analysis; Recognition: detection
Image segmentation
retrieval
Segmentation
Semantics
Shape
Transformers
ISSN:1063-6919
Online Access:Get full text
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Description
Summary:Image segmentation groups pixels with different semantics, e.g., category or instance membership. Each choice of semantics defines a task. While only the semantics of each task differ, current research focuses on designing spe-cialized architectures for each task. We present Masked- attention Mask Transformer (Mask2Former), a new archi-tecture capable of addressing any image segmentation task (panoptic, instance or semantic). Its key components in-clude masked attention, which extracts localized features by constraining cross-attention within predicted mask regions. In addition to reducing the research effort by at least three times, it outperforms the best specialized architectures by a significant margin on four popular datasets. Most no-tably, Mask2Former sets a new state-of-the-art for panoptic segmentation (57.8 PQ on COCO), instance segmentation (50.1 AP on COCO) and semantic segmentation (57.7 mIoU onADE20K).
ISSN:1063-6919
DOI:10.1109/CVPR52688.2022.00135