mlsanityData to 7 Oct 2026

Fully Convolutional Networks for Semantic Segmentation

arXiv 1411.4038 ↗First posted 14 Nov 2014cs.CVcited by 4,845

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Cited by 89 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation. Our key insight is to build "fully convolutional" networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet, the VGG net, and GoogLeNet) into fully convolutional networks and transfer their learned representations by fine-tuning to the segmentation task. We then define a novel architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achieves state-of-the-art segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes one third of a second for a typical image.

Key facts. Cited by 4,845 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Nov 2014.

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Citing papers per year: 2018: 4; 2019: 3; 2020: 0; 2021: 1229; 2022: 1071; 2023: 961; 2024: 751; 2025: 541; 2026: 285.

4,845 papers.

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  1. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
  2. 2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,338
  3. 2021Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionscited by 1,960
  4. 2021Vision Transformers for Dense Predictioncited by 1,789
  5. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  6. 2022CoCa: Contrastive Captioners are Image-Text Foundation Modelscited by 1,265
  7. 2021Per-Pixel Classification is Not All You Need for Semantic Segmentationcited by 1,148
  8. 2021Grounded Language-Image Pre-trainingcited by 1,147
  9. 2021CLIP-Adapter: Better Vision-Language Models with Feature Adapterscited by 1,021
  10. 2021Segmenter: Transformer for Semantic Segmentationcited by 855
  11. 2023Vision-Language Models for Vision Tasks: A Surveycited by 834
  12. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
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  1. 2023LISA: Reasoning Segmentation via Large Language Modelcited by 770
  2. 2021Domain Generalization: A Surveycited by 760
  3. 2021MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformercited by 636
  4. 2021Perceiver IO: A General Architecture for Structured Inputs & Outputscited by 616
  5. 2021KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3Dcited by 570
  6. 2022Open-Vocabulary Semantic Segmentation with Mask-adapted CLIPcited by 560
  7. 2023Segment Everything Everywhere All at Oncecited by 546
  8. 2021DenseCLIP: Language-Guided Dense Prediction with Context-Aware Promptingcited by 530
  9. 2022GroupViT: Semantic Segmentation Emerges from Text Supervisioncited by 529
  10. 2021Image Segmentation Using Text and Image Promptscited by 509
  11. 2022MaxViT: Multi-Axis Vision Transformercited by 476
  12. 2021Semi-Supervised Semantic Segmentation with Cross Pseudo Supervisioncited by 462
  13. 2021PointCLIP: Point Cloud Understanding by CLIPcited by 461
  14. 2021Deep Long-Tailed Learning: A Surveycited by 416
  15. 2021Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigmcited by 405
  16. 2021Early Convolutions Help Transformers See Bettercited by 395
  17. 2022Visual Language Maps for Robot Navigationcited by 362
  18. 2021CRIS: CLIP-Driven Referring Image Segmentationcited by 353
  19. 2021LAVT: Language-Aware Vision Transformer for Referring Image Segmentationcited by 347
  20. 2022Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentationcited by 341
  21. 2022OneFormer: One Transformer to Rule Universal Image Segmentationcited by 327
  22. 2021Categorical Depth Distribution Network for Monocular 3D Object Detectioncited by 326
  23. 2021Pre-Trained Models: Past, Present and Futurecited by 319
  24. 2023VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completioncited by 306
  25. 2021DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentationcited by 299
  26. 2023Side Adapter Network for Open-Vocabulary Semantic Segmentationcited by 296
  27. 2022Generalized Decoding for Pixel, Image, and Languagecited by 292
  28. 2023OpenMask3D: Open-Vocabulary 3D Instance Segmentationcited by 288
  29. 2021HDMapNet: An Online HD Map Construction and Evaluation Frameworkcited by 280
  30. 2021SegDiff: Image Segmentation with Diffusion Probabilistic Modelscited by 279
  31. 2024A Survey on Vision-Language-Action Models for Embodied AIcited by 274
  32. 2021Video Transformer Networkcited by 271
  33. 2021TransBTS: Multimodal Brain Tumor Segmentation Using Transformercited by 265
  34. 2021LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentationcited by 259
  35. 2022SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentationcited by 252
  36. 2023GRES: Generalized Referring Expression Segmentationcited by 252
  37. 2023Personalize Segment Anything Model with One Shotcited by 250
  38. 2021Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalizationcited by 248

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