Methods › Computer Vision › Semantic Segmentation Models › UCTransNet

UCTransNet

4 papers tagged archive 2025-07-28

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

UCTransNet is an end-to-end deep learning network for semantic segmentation that takes U-Net as the main structure of the network. The original skip connections of U-Net are replaced by CTrans consisting of two components: Channel-wise Cross fusion Transformer (CCT) and Channel-wise Cross Attention (CCA) to guide the fused multi-Scale channel-wise information to effectively connect to the decoder features for eliminating the ambiguity.

Source: UCTransNet: Rethinking the Skip Connections in U-Net...

Papers archive 2025-07-28

4 shown of 4, 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

8 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
Image Segmentation4
Medical Image Segmentation4
Semantic Segmentation4
Segmentation2
Decoder1
Pseudo Label1
UNET Segmentation1
text annotation1

Usage over time archive 2025-07-28

Papers per year tagged with UCTransNet: 2021 to 2024, peak 1 1 0 2021: 1 paper 2021 2022: 1 paper 2022 2023: 1 paper 2023 2024: 1 paper 2024
Papers per year the archive tags with this method, by the paper's archive date (4 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

Semantic Segmentation Models

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