Data to 14 Oct 2026

RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

arXiv 1611.06612 ↗First posted 20 Nov 2016cs.CVcited by 465

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Cited by 21 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense classification problems such as semantic segmentation. However, repeated subsampling operations like pooling or convolution striding in deep CNNs lead to a significant decrease in the initial image resolution. Here, we present RefineNet, a generic multi-path refinement network that explicitly exploits all the information available along the down-sampling process to enable high-resolution prediction using long-range residual connections. In this way, the deeper layers that capture high-level semantic features can be directly refined using fine-grained features from earlier convolutions. The individual components of RefineNet employ residual connections following the identity mapping mindset, which allows for effective end-to-end training. Further, we introduce chained residual pooling, which captures rich background context in an efficient manner. We carry out comprehensive experiments and set new state-of-the-art results on seven public datasets. In particular, we achieve an intersection-over-union score of 83.4 on the challenging PASCAL VOC 2012 dataset, which is the best reported result to date.

Key facts. Cited by 465 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Nov 2016.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 1; 2021: 147; 2022: 120; 2023: 88; 2024: 54; 2025: 31; 2026: 23.

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2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
2021Vision Transformers for Dense Predictioncited by 1,803
2020Improved Techniques for Training Score-Based Generative Modelscited by 1,043
2022Diffusion Models in Vision: A Surveycited by 1,015
2021Segmenter: Transformer for Semantic Segmentationcited by 855
2023Segment Anything in High Qualitycited by 376
2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 286
2022SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentationcited by 252
2021Exploring Cross-Image Pixel Contrast for Semantic Segmentationcited by 242
2025Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcementcited by 239
2022CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformerscited by 231

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2023SegGPT: Segmenting Everything In Contextcited by 194
2022Rethinking Semantic Segmentation: A Prototype Viewcited by 140
2021Adversarial purification with Score-based generative modelscited by 134
2022BEVSegFormer: Bird's Eye View Semantic Segmentation From Arbitrary Camera Rigscited by 123
2022Learning What Not to Segment: A New Perspective on Few-Shot Segmentationcited by 120
2022Cross-Image Relational Knowledge Distillation for Semantic Segmentationcited by 118
2023Delivering Arbitrary-Modal Semantic Segmentationcited by 117
2026V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learningcited by 117
2022ReSTR: Convolution-free Referring Image Segmentation Using Transformerscited by 112
2022SegViT: Semantic Segmentation with Plain Vision Transformerscited by 106
2022An Empirical Study of Remote Sensing Pretrainingcited by 105
2021Locate then Segment: A Strong Pipeline for Referring Image Segmentationcited by 98
2022Self-Support Few-Shot Semantic Segmentationcited by 96
2022Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusioncited by 94
2022SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentationcited by 87
2022GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Modelscited by 87
2022Deep Hierarchical Semantic Segmentationcited by 84
2022PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllerscited by 84
2021DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentationcited by 80
2021A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentationcited by 79
2021Re-distributing Biased Pseudo Labels for Semi-supervised Semantic Segmentation: A Baseline Investigationcited by 78
2022Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and Applicationscited by 78
20213D Semantic Scene Completion: a Surveycited by 74
2022Representation Compensation Networks for Continual Semantic Segmentationcited by 72
2021Mining Latent Classes for Few-shot Segmentationcited by 69
2022Open-world Semantic Segmentation via Contrasting and Clustering Vision-Language Embeddingcited by 67
2021InverseForm: A Loss Function for Structured Boundary-Aware Segmentationcited by 65
2021Lite Vision Transformer with Enhanced Self-Attentioncited by 64
2023FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Modelscited by 61
2024Multi-Task Dense Prediction via Mixture of Low-Rank Expertscited by 58
2022L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic Segmentationcited by 57
2021Learning Meta-class Memory for Few-Shot Semantic Segmentationcited by 56
2023Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimizationcited by 56
2021High-Resolution Image Harmonization via Collaborative Dual Transformationscited by 55
2021Beyond Classification: Directly Training Spiking Neural Networks for Semantic Segmentationcited by 55
2021SeMask: Semantically Masked Transformers for Semantic Segmentationcited by 55
2023LiDAR Data Synthesis with Denoising Diffusion Probabilistic Modelscited by 55
2022Pro-tuning: Unified Prompt Tuning for Vision Taskscited by 53

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