mlsanityData to 7 Oct 2026

Spatial-context-aware deep neural network for multi-class image classification

arXiv 2111.12296 ↗First posted 24 Nov 2021cs.CVcited by 4

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Abstract (arXiv)

Multi-label image classification is a fundamental but challenging task in computer vision. Over the past few decades, solutions exploring relationships between semantic labels have made great progress. However, the underlying spatial-contextual information of labels is under-exploited. To tackle this problem, a spatial-context-aware deep neural network is proposed to predict labels taking into account both semantic and spatial information. This proposed framework is evaluated on Microsoft COCO and PASCAL VOC, two widely used benchmark datasets for image multi-labelling. The results show that the proposed approach is superior to the state-of-the-art solutions on dealing with the multi-label image classification problem.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 20 of its 37 references that are in the map. First posted on arXiv 24 Nov 2021.

Builds on

20 of 37 references.

References · 20

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  4. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  5. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  6. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  7. 2014Going Deeper with Convolutionscited by 4,231
  8. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  9. 2019Multi-Label Image Recognition with Graph Convolutional Networkscited by 320
  10. 2016CNN-RNN: A Unified Framework for Multi-label Image Classificationcited by 189
  11. 2020General Multi-label Image Classification with Transformerscited by 126
  12. 2017Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classificationcited by 85
8 more
  1. 2017Multi-label Image Recognition by Recurrently Discovering Attentional Regionscited by 83
  2. 2019Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classificationcited by 78
  3. 2019Multi-Label Classification with Label Graph Superimposingcited by 65
  4. 2015Exploit Bounding Box Annotations for Multi-label Object Recognitioncited by 39
  5. 2019Putting visual object recognition in contextcited by 33
  6. 2017Learning Deep Latent Spaces for Multi-Label Classificationcited by 21
  7. 2019CAGNet: Content-Aware Guidance for Salient Object Detectioncited by 16
  8. 2018Adversarial Learning of Label Dependency: A Novel Framework for Multi-class Classificationcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 1.

4 papers.

  1. 2023Scale Optimization Using Evolutionary Reinforcement Learning for Object Detection on Drone Imagerycited by 5
  2. 2024Position: Do Not Explain Vision Models Without Contextcited by 2
  3. 2025Face recognition on point cloud with cgan-top for denoisingcited by 0
  4. 2026Rethinking Multi-Label Image Classification With Deep Learning: Taxonomy, Challenge, and Outlookcited by 0