mlsanityData to 14 Oct 2026

Hypergraph Convolutional Networks for Weakly-Supervised Semantic Segmentation

arXiv 2210.05564 ↗First posted 11 Oct 2022cs.CVcited by 5

Not on the map. Why?

Abstract (arXiv)

Semantic segmentation is a fundamental topic in computer vision. Several deep learning methods have been proposed for semantic segmentation with outstanding results. However, these models require a lot of densely annotated images. To address this problem, we propose a new algorithm that uses HyperGraph Convolutional Networks for Weakly-supervised Semantic Segmentation (HyperGCN-WSS). Our algorithm constructs spatial and k-Nearest Neighbor (k-NN) graphs from the images in the dataset to generate the hypergraphs. Then, we train a specialized HyperGraph Convolutional Network (HyperGCN) architecture using some weak signals. The outputs of the HyperGCN are denominated pseudo-labels, which are later used to train a DeepLab model for semantic segmentation. HyperGCN-WSS is evaluated on the PASCAL VOC 2012 dataset for semantic segmentation, using scribbles or clicks as weak signals. Our algorithm shows competitive performance against previous methods.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 19 of its 27 references that are in the map. First posted on arXiv 11 Oct 2022.

Builds on

19 of 27 references.

References · 19

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7 more
  1. 2019DeepGCNs: Can GCNs Go as Deep as CNNs?cited by 527
  2. 2016ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentationcited by 331
  3. 2019Hypergraph Convolution and Hypergraph Attentioncited by 228
  4. 2020Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddingscited by 214
  5. 2018Normalized Cut Loss for Weakly-supervised CNN Segmentationcited by 112
  6. 2018Learning random-walk label propagation for weakly-supervised semantic segmentationcited by 101
  7. 2021Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentationcited by 47

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 1.

5 papers.

  1. 2024WiGNet: Windowed Vision Graph Neural Networkcited by 8
  2. 2025Recent Advances in Hypergraph Neural Networkscited by 7
  3. 2024GABIC: Graph-based Attention Block for Image Compressioncited by 4
  4. 2025Scalable Hypergraph Structure Learning with Diverse Smoothness Priorscited by 1
  5. 2026StegGNN: Learning Graphical Representation for Image Steganographycited by 0