mlsanityData to 14 Oct 2026

Dilated Residual Networks

arXiv 1705.09914 ↗First posted 28 May 2017cs.CVcited by 294

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Abstract (arXiv)

Convolutional networks for image classification progressively reduce resolution until the image is represented by tiny feature maps in which the spatial structure of the scene is no longer discernible. Such loss of spatial acuity can limit image classification accuracy and complicate the transfer of the model to downstream applications that require detailed scene understanding. These problems can be alleviated by dilation, which increases the resolution of output feature maps without reducing the receptive field of individual neurons. We show that dilated residual networks (DRNs) outperform their non-dilated counterparts in image classification without increasing the model's depth or complexity. We then study gridding artifacts introduced by dilation, develop an approach to removing these artifacts (`degridding'), and show that this further increases the performance of DRNs. In addition, we show that the accuracy advantage of DRNs is further magnified in downstream applications such as object localization and semantic segmentation.

Key facts. Cited by 294 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 May 2017.

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Citing papers per year: 2021: 92; 2022: 80; 2023: 45; 2024: 38; 2025: 29; 2026: 10.

294 papers.

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  1. 2021Accelerating Sparse Deep Neural Networkscited by 223
  2. 2021ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Biascited by 216
  3. 2022Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlationcited by 197
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  5. 2021Conformer: Local Features Coupling Global Representations for Visual Recognitioncited by 164
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  8. 2022Semantic Image Synthesis via Diffusion Modelscited by 132
  9. 2022ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyondcited by 117
  10. 2021Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learningcited by 101
  11. 2021Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representationscited by 96
  12. 2022Self-Support Few-Shot Semantic Segmentationcited by 96
38 more
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  2. 2023iDisc: Internal Discretization for Monocular Depth Estimationcited by 73
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  4. 2021Multiple Object Tracking with Correlation Learningcited by 67
  5. 2022In Defense of the Unitary Scalarization for Deep Multi-Task Learningcited by 67
  6. 2022Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?cited by 67
  7. 2021The Spatially-Correlative Loss for Various Image Translation Taskscited by 66
  8. 2021On the Practicality of Deterministic Epistemic Uncertaintycited by 62
  9. 2022Bridging the Domain Gap for Multi-Agent Perceptioncited by 60
  10. 2021Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth Estimationcited by 55
  11. 2021Look Before You Leap: Learning Landmark Features for One-Stage Visual Groundingcited by 52
  12. 2021Multimodal Image Synthesis and Editing: The Generative AI Eracited by 52
  13. 2021ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imagingcited by 51
  14. 2021Capturing Omni-Range Context for Omnidirectional Segmentationcited by 50
  15. 2021Semi-Supervised Active Learning with Temporal Output Discrepancycited by 50
  16. 2021Diverse Semantic Image Synthesis via Probability Distribution Modelingcited by 49
  17. 2021Mining Contextual Information Beyond Image for Semantic Segmentationcited by 48
  18. 2021Style Normalization and Restitution for Domain Generalization and Adaptationcited by 46
  19. 2022Transformers in Time-series Analysis: A Tutorialcited by 46
  20. 2021Instance-wise Hard Negative Example Generation for Contrastive Learning in Unpaired Image-to-Image Translationcited by 45
  21. 2021Image Synthesis via Semantic Compositioncited by 45
  22. 2022Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Modelscited by 45
  23. 2022Exploring Patch-wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Taskscited by 42
  24. 2023Detecting Human-Object Contact in Imagescited by 41
  25. 2023Generative Semantic Communication: Diffusion Models Beyond Bit Recoverycited by 41
  26. 2021ISNet: Integrate Image-Level and Semantic-Level Context for Semantic Segmentationcited by 40
  27. 2022NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Resultscited by 40
  28. 2023Egocentric Audio-Visual Object Localizationcited by 40
  29. 2021Boundary-aware Transformers for Skin Lesion Segmentationcited by 39
  30. 2021ACDNet: Adaptively Combined Dilated Convolution for Monocular Panorama Depth Estimationcited by 39
  31. 2022QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translationcited by 39
  32. 2021Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluationscited by 38
  33. 2021Teachers Do More Than Teach: Compressing Image-to-Image Modelscited by 37
  34. 2021DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptationcited by 37
  35. 2024Frequency-Adaptive Dilated Convolution for Semantic Segmentationcited by 34
  36. 2021Consistency-based Active Learning for Object Detectioncited by 32
  37. 2022TeST: Test-time Self-Training under Distribution Shiftcited by 29
  38. 2021Attention meets Geometry: Geometry Guided Spatial-Temporal Attention for Consistent Self-Supervised Monocular Depth Estimationcited by 27

50 most cited of 294 All 294 (JSON)