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Self-supervised Equivariant Attention Mechanism

SEAM

14 papers tagged archive 2025-07-28

Introduced by Yude Wang et al. in Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Self-supervised Equivariant Attention Mechanism, or SEAM, is an attention mechanism for weakly supervised semantic segmentation. The SEAM applies consistency regularization on CAMs from various transformed images to provide self-supervision for network learning. To further improve the network prediction consistency, SEAM introduces the pixel correlation module (PCM), which captures context appearance information for each pixel and revises original CAMs by learned affinity attention maps. The SEAM is implemented by a siamese network with equivariant cross regularization (ECR) loss, which regularizes the original CAMs and the revised CAMs on different branches.

PaperSource

Papers archive 2025-07-28

14 shown of 14, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 31 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Semantic Segmentation4
Weakly supervised Semantic Segmentation4
Weakly-Supervised Semantic Segmentation4
Contrastive Learning2
Segmentation2
Bayesian Inference1
Continual Learning1
Coreference Resolution1
Data Augmentation1
Decoder1
Event Detection1
Face Detection1
Facies Classification1
Image Classification1
Image Segmentation1
Language Modeling1
Language Modelling1
Multi-hop Question Answering1
Optical Character Recognition (OCR)1
Prognosis1

Usage over time archive 2025-07-28

Papers per year tagged with SEAM: 2020 to 2025, peak 4 4 0 2020: 1 paper 2020 2021: 2 papers 2021 2022: 4 papers 2022 2023: 4 papers 2023 2024: 2 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (14 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Attention Mechanisms

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