mlsanityData to 14 Oct 2026

Golden Cudgel Network for Real-Time Semantic Segmentation

arXiv 2503.03325 ↗First posted 5 Mar 2025cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

Recent real-time semantic segmentation models, whether single-branch or multi-branch, achieve good performance and speed. However, their speed is limited by multi-path blocks, and some depend on high-performance teacher models for training. To overcome these issues, we propose Golden Cudgel Network (GCNet). Specifically, GCNet uses vertical multi-convolutions and horizontal multi-paths for training, which are reparameterized into a single convolution for inference, optimizing both performance and speed. This design allows GCNet to self-enlarge during training and self-contract during inference, effectively becoming a "teacher model" without needing external ones. Experimental results show that GCNet outperforms existing state-of-the-art models in terms of performance and speed on the Cityscapes, CamVid, and Pascal VOC 2012 datasets. The code is available at https://github.com/gyyang23/GCNet.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 28 of its 41 references that are in the map. First posted on arXiv 5 Mar 2025.

Builds on

28 of 41 references.

References · 28

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  4. 2017Squeeze-and-Excitation Networkscited by 4,789
  5. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
  6. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,241
  7. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
  8. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
  9. 2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,351
  10. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,161
  11. 2021Per-Pixel Classification is Not All You Need for Semantic Segmentationcited by 1,152
  12. 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,133
16 more
  1. 2018Dual Attention Network for Scene Segmentationcited by 1,003
  2. 2018BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentationcited by 645
  3. 2016Training Region-based Object Detectors with Online Hard Example Miningcited by 579
  4. 2019ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networkscited by 575
  5. 2020BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentationcited by 294
  6. 2017ICNet for Real-Time Semantic Segmentation on High-Resolution Imagescited by 261
  7. 2019ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blockscited by 178
  8. 2019Fast-SCNN: Fast Semantic Segmentation Networkcited by 125
  9. 2021Rethinking BiSeNet For Real-time Semantic Segmentationcited by 124
  10. 2019In Defense of Pre-trained ImageNet Architectures for Real-time Semantic Segmentation of Road-driving Imagescited by 100
  11. 2018CGNet: A Light-weight Context Guided Network for Semantic Segmentationcited by 94
  12. 2021Diverse Branch Block: Building a Convolution as an Inception-like Unitcited by 87
  13. 2022PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllerscited by 84
  14. 2020HyperSeg: Patch-wise Hypernetwork for Real-time Semantic Segmentationcited by 66
  15. 2022PP-LiteSeg: A Superior Real-Time Semantic Segmentation Modelcited by 33
  16. 2023SCTNet: Single-Branch CNN with Transformer Semantic Information for Real-Time Segmentationcited by 22

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Vehicle-centric Perception via Multimodal Structured Pre-trainingcited by 2
  2. 2025Segment Any Vehicle: Semantic and Visual Context Driven SAM and A Benchmarkcited by 1
  3. 2026RSGMamba: Reliability-Aware Self-Gated State Space Model for Multimodal Semantic Segmentationcited by 1
  4. 2026Prompt-Free and Efficient SAM2 Adaptation for Biomedical Semantic Segmentation via Dual Adapterscited by 1