Masked-attention Mask Transformer for Universal Image Segmentation
Open accessAbstract
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).
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CLFT uses a bidirectional network and cross-fusion strategy to integrate data at the Transformer decoder layer, enhancing accuracy and robustness of semantic segmentation in autonomous driving.
“The model utilizes a bidirectional network and a cross-fusion strategy to integrate data from both sensors at the Transformer decoder layer, enhancing the accuracy and robustness of semantic segmentation in autonomous driving.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [61]
CLFT fuses camera and LiDAR data by projecting LiDAR point clouds onto the camera plane, upsampling them into 2D feature maps, and inputting them with camera images into a Transformer-based network.
“The CLFT [61] model achieves the fusion of camera and LiDAR data by projecting Li-DAR point clouds onto the camera plane, upsampling them into 2D feature maps, and then inputting them together with camera images into a Transformer-based network.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [61]
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- Authors
- Cheng, B., Misra, I., Schwing, A. G., Kirillov, A., & Girdhar, R.
- Affiliations
- Meta (United States) (US)
- University of Illinois Urbana-Champaign (US)
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 1280-1289
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4226069476
- Semantic Scholar
- 658a017302d29e4acf4ca789cb5d9f27983717ff
- Cited by
- 3,565
- References
- 41
- Citations by year
- 2026: 907 · 2025: 1179 · 2024: 973 · 2023: 456 · 2022: 49 · 2019: 1
- FWCI
- 240.91
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- NSF (1718221,2008387,2045586,2106825)
- MRI (1725729)
- NIFA (2020-67021-32799)
- Cisco Systems Inc. (1377144)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- Mask2Former, attention masking, cross-attention, panoptic segmentation, instance segmentation, semantic segmentation
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