Data to 14 Oct 2026

Dual Attention Network for Scene Segmentation

arXiv 1809.02983 ↗First posted 9 Sep 2018cs.CVcited by 1,003

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Abstract (arXiv)

In this paper, we address the scene segmentation task by capturing rich contextual dependencies based on the selfattention mechanism. Unlike previous works that capture contexts by multi-scale features fusion, we propose a Dual Attention Networks (DANet) to adaptively integrate local features with their global dependencies. Specifically, we append two types of attention modules on top of traditional dilated FCN, which model the semantic interdependencies in spatial and channel dimensions respectively. The position attention module selectively aggregates the features at each position by a weighted sum of the features at all positions. Similar features would be related to each other regardless of their distances. Meanwhile, the channel attention module selectively emphasizes interdependent channel maps by integrating associated features among all channel maps. We sum the outputs of the two attention modules to further improve feature representation which contributes to more precise segmentation results. We achieve new state-of-the-art segmentation performance on three challenging scene segmentation datasets, i.e., Cityscapes, PASCAL Context and COCO Stuff dataset. In particular, a Mean IoU score of 81.5% on Cityscapes test set is achieved without using coarse data. We make the code and trained model publicly available at https://github.com/junfu1115/DANet

Key facts. Cited by 1,003 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Sep 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 323; 2022: 212; 2023: 206; 2024: 143; 2025: 81; 2026: 38.

1,003 papers.

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2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,351
2021Per-Pixel Classification is Not All You Need for Semantic Segmentationcited by 1,152
2021Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNetcited by 1,101
2021UNETR: Transformers for 3D Medical Image Segmentationcited by 959
2021Segmenter: Transformer for Semantic Segmentationcited by 855
2021Multi-Stage Progressive Image Restorationcited by 816
2023LISA: Reasoning Segmentation via Large Language Modelcited by 776
2021MonoScene: Monocular 3D Semantic Scene Completioncited by 363
2021CRIS: CLIP-Driven Referring Image Segmentationcited by 353
2022Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detectioncited by 317

38 more

2022SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentationcited by 252
2021Exploring Cross-Image Pixel Contrast for Semantic Segmentationcited by 242
2021Coordinate Attention for Efficient Mobile Network Designcited by 241
2021Attention Mechanisms in Computer Vision: A Surveycited by 232
2022CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformerscited by 231
2021Learning to Estimate Hidden Motions with Global Motion Aggregationcited by 224
2022Visual Attention Networkcited by 210
2021Adaptive Prototype Learning and Allocation for Few-Shot Segmentationcited by 207
2023Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIPcited by 201
2021Simultaneously Localize, Segment and Rank the Camouflaged Objectscited by 198
2022DaViT: Dual Attention Vision Transformerscited by 175
2022Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communicationscited by 175
2021Source-Free Domain Adaptation for Semantic Segmentationcited by 168
2021UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagerycited by 167
2023Towards Open Vocabulary Learning: A Surveycited by 166
2021VOLO: Vision Outlooker for Visual Recognitioncited by 165
2022HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentationcited by 164
2022Advancing Plain Vision Transformer Towards Remote Sensing Foundation Modelcited by 149
2021Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videoscited by 141
2021PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localizationcited by 133
2021CycleMLP: A MLP-like Architecture for Dense Predictioncited by 126
2021Transformer-Based Attention Networks for Continuous Pixel-Wise Predictioncited by 125
2023Transformer-Based Visual Segmentation: A Surveycited by 124
2023Dual Aggregation Transformer for Image Super-Resolutioncited by 124
2021Generative Adversarial Transformerscited by 120
2021MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detectioncited by 117
2023Delivering Arbitrary-Modal Semantic Segmentationcited by 117
2021UTNet: A Hybrid Transformer Architecture for Medical Image Segmentationcited by 116
2021Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformercited by 115
2021Uncertainty-aware Joint Salient Object and Camouflaged Object Detectioncited by 114
2021Semi-supervised Semantic Segmentation with Directional Context-aware Consistencycited by 112
2021Image Manipulation Detection by Multi-View Multi-Scale Supervisioncited by 111
2021Encoder Fusion Network with Co-Attention Embedding for Referring Image Segmentationcited by 111
2021Beyond Self-attention: External Attention using Two Linear Layers for Visual Taskscited by 109
2022SegViT: Semantic Segmentation with Plain Vision Transformerscited by 106
2022An Empirical Study of Remote Sensing Pretrainingcited by 105
2021Cross-Modal Progressive Comprehension for Referring Segmentationcited by 102
2021AS-MLP: An Axial Shifted MLP Architecture for Visioncited by 101

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