mlsanityData to 7 Oct 2026

Rethinking Atrous Convolution for Semantic Image Segmentation

arXiv 1706.05587 ↗First posted 17 Jun 2017cs.CVcited by 2,154

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Cited by 46 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In this work, we revisit atrous convolution, a powerful tool to explicitly adjust filter's field-of-view as well as control the resolution of feature responses computed by Deep Convolutional Neural Networks, in the application of semantic image segmentation. To handle the problem of segmenting objects at multiple scales, we design modules which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates. Furthermore, we propose to augment our previously proposed Atrous Spatial Pyramid Pooling module, which probes convolutional features at multiple scales, with image-level features encoding global context and further boost performance. We also elaborate on implementation details and share our experience on training our system. The proposed `DeepLabv3' system significantly improves over our previous DeepLab versions without DenseCRF post-processing and attains comparable performance with other state-of-art models on the PASCAL VOC 2012 semantic image segmentation benchmark.

Key facts. Cited by 2,154 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jun 2017.

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Citing papers per year: 2021: 479; 2022: 445; 2023: 454; 2024: 362; 2025: 281; 2026: 133.

2,154 papers.

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  1. 2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,338
  2. 2021Vision Transformers for Dense Predictioncited by 1,789
  3. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  4. 2021Per-Pixel Classification is Not All You Need for Semantic Segmentationcited by 1,148
  5. 2021Segmenter: Transformer for Semantic Segmentationcited by 855
  6. 2022Language-driven Semantic Segmentationcited by 711
  7. 2021MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformercited by 636
  8. 2023Segment Everything Everywhere All at Oncecited by 546
  9. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  10. 2021Label-Efficient Semantic Segmentation with Diffusion Modelscited by 495
  11. 2022InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutionscited by 479
  12. 2021A White Paper on Neural Network Quantizationcited by 453
38 more
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  2. 2021LAVT: Language-Aware Vision Transformer for Referring Image Segmentationcited by 347
  3. 2021Categorical Depth Distribution Network for Monocular 3D Object Detectioncited by 326
  4. 2021HuMoR: 3D Human Motion Model for Robust Pose Estimationcited by 319
  5. 2022Generalized Decoding for Pixel, Image, and Languagecited by 292
  6. 2022SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentationcited by 252
  7. 2023Multimodal Foundation Models: From Specialists to General-Purpose Assistantscited by 247
  8. 2021Exploring Cross-Image Pixel Contrast for Semantic Segmentationcited by 242
  9. 2022Unsupervised Semantic Segmentation by Distilling Feature Correspondencescited by 241
  10. 2021Coordinate Attention for Efficient Mobile Network Designcited by 239
  11. 2025Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcementcited by 234
  12. 2021A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Posecited by 233
  13. 2021ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Biascited by 216
  14. 2021RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whiteningcited by 213
  15. 2021K-Net: Towards Unified Image Segmentationcited by 211
  16. 2022Knowledge Distillation from A Stronger Teachercited by 204
  17. 2021Deep Deterministic Uncertainty: A Simple Baselinecited by 200
  18. 2021Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentationcited by 200
  19. 2023Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIPcited by 199
  20. 2021Distribution Alignment: A Unified Framework for Long-tail Visual Recognitioncited by 198
  21. 2021Unsupervised Semantic Segmentation by Contrasting Object Mask Proposalscited by 190
  22. 2023Semantic-SAM: Segment and Recognize Anything at Any Granularitycited by 185
  23. 2022Generating Holistic 3D Human Motion from Speechcited by 180
  24. 2022ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentationcited by 178
  25. 2023ZipIt! Merging Models from Different Tasks without Trainingcited by 178
  26. 2021Source-Free Domain Adaptation for Semantic Segmentationcited by 168
  27. 2023Towards Open Vocabulary Learning: A Surveycited by 165
  28. 2022SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understandingcited by 164
  29. 2022HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentationcited by 163
  30. 2022Separable Self-attention for Mobile Vision Transformerscited by 163
  31. 20222DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Cloudscited by 157
  32. 2022Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNscited by 155
  33. 2024ControlNet++: Improving Conditional Controls with Efficient Consistency Feedbackcited by 153
  34. 2021HRFormer: High-Resolution Transformer for Dense Predictioncited by 149
  35. 2022Model-Based Imitation Learning for Urban Drivingcited by 149
  36. 2023Foundation Models for Generalist Geospatial Artificial Intelligencecited by 143
  37. 2021Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalizationcited by 141
  38. 2021Robust High-Resolution Video Matting with Temporal Guidancecited by 136

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