Methods › Computer Vision › Semantic Segmentation Modules › STDC
Short-Term Dense Concatenate
STDC
Introduced by Mingyuan Fan et al. in Rethinking BiSeNet For Real-time Semantic Segmentation
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
STDC, or Short-Term Dense Concatenate, is a module for semantic segmentation to extract deep features with scalable receptive field and multi-scale information. It aims to remove structure redundancy in the BiSeNet architecture, specifically BiSeNet adds an extra path to encode spatial information which can be time-consuming,. Instead, STDC gradually reduces the dimension of feature maps and use the aggregation of them for image representation.
We concatenate response maps from multiple continuous layers, each of which encodes input image/feature in different scales and respective fields, leading to multi-scale feature representation. To speed up, the filter size of layers is gradually reduced with negligible loss in segmentation performance.
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.
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Make a Strong Teacher with Label Assistance: A Novel Knowledge Distillation Approach for Semantic Segmentation 18 Jul 2024 · 1 repository · arXiv:2407.13254
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Lightweight and Progressively-Scalable Networks for Semantic Segmentation 27 Jul 2022 · 1 repository · arXiv:2207.13600
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SFNet: Faster, Accurate, and Domain Agnostic Semantic Segmentation via Semantic Flow 10 Jul 2022 · 1 repository · arXiv:2207.04415
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Rethinking BiSeNet For Real-time Semantic Segmentation 27 Apr 2021 · 6 repositories · arXiv:2104.13188Syntology ran 8 of 16 samples · 8 unverified
Tasks archive 2025-07-28
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Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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