mlsanityData to 7 Oct 2026

Fully Connected Deep Structured Networks

arXiv 1503.02351 ↗First posted 9 Mar 2015cs.CV cs.LGcited by 24

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Abstract (arXiv)

Convolutional neural networks with many layers have recently been shown to achieve excellent results on many high-level tasks such as image classification, object detection and more recently also semantic segmentation. Particularly for semantic segmentation, a two-stage procedure is often employed. Hereby, convolutional networks are trained to provide good local pixel-wise features for the second step being traditionally a more global graphical model. In this work we unify this two-stage process into a single joint training algorithm. We demonstrate our method on the semantic image segmentation task and show encouraging results on the challenging PASCAL VOC 2012 dataset.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Mar 2015.

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Citing papers per year: 2021: 6; 2022: 5; 2023: 5; 2024: 3; 2025: 5.

24 papers.

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  1. 2021Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safetycited by 35
  2. 2023Semantic Image Segmentation: Two Decades of Researchcited by 26
  3. 2022On the Road to Online Adaptation for Semantic Image Segmentationcited by 23
  4. 2021Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Surveycited by 19
  5. 2021Deep Structured Reactive Planningcited by 18
  6. 2023BPKD: Boundary Privileged Knowledge Distillation For Semantic Segmentationcited by 15
  7. 2021Context-self contrastive pretraining for crop type semantic segmentationcited by 11
  8. 2025LIFe-GoM: Generalizable Human Rendering with Learned Iterative Feedback Over Multi-Resolution Gaussians-on-Meshcited by 11
  9. 2021Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyondcited by 10
  10. 2022Vision-based Large-scale 3D Semantic Mapping for Autonomous Driving Applicationscited by 4
  11. 2022U-HRNet: Delving into Improving Semantic Representation of High Resolution Network for Dense Predictioncited by 3
  12. 2025Impact of Loss Weight and Model Complexity on Physics-Informed Neural Networks for Computational Fluid Dynamicscited by 2
12 more
  1. 2021Border-SegGCN: Improving Semantic Segmentation by Refining the Border Outline using Graph Convolutional Networkcited by 1
  2. 2022Artificial Intelligence-Based Analytics for Impacts of COVID-19 and Online Learning on College Students' Mental Healthcited by 1
  3. 2022SOInter: A Novel Deep Energy Based Interpretation Method for Explaining Structured Output Modelscited by 1
  4. 2023Deep Dependency Networks for Multi-Label Classificationcited by 1
  5. 2023Latent Graph Attention for Enhanced Spatial Contextcited by 1
  6. 2023TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Startcited by 1
  7. 2024Physics-informed Deep Learning to Solve Three-dimensional Terzaghi Consolidation Equation: Forward and Inverse Problemscited by 1
  8. 2024Fine-Grained Scene Graph Generation via Sample-Level Bias Predictioncited by 1
  9. 2025Heatmap Regression without Soft-Argmax for Facial Landmark Detectioncited by 1
  10. 2025Scaling and context steer LLMs along the same computational path as the human braincited by 1
  11. 2024Deep-learning recognition and tracking of individual nanotubes in low-contrast microscopy videoscited by 0
  12. 2025Finite-Time Global Optimality Convergence in Deep Neural Actor-Critic Methods for Decentralized Multi-Agent Reinforcement Learningcited by 0