Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentation
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T. Gevers | Shaodi You | Qi Bi
[1] Zhiwei Xiong,et al. Style Projected Clustering for Domain Generalized Semantic Segmentation , 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[2] B. Schiele,et al. HGFormer: Hierarchical Grouping Transformer for Domain Generalized Semantic Segmentation , 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Chunhua Shen,et al. SegGPT: Segmenting Everything In Context , 2023, ArXiv.
[4] Ross B. Girshick,et al. Segment Anything , 2023, 2023 IEEE/CVF International Conference on Computer Vision (ICCV).
[5] Fabrizio J. Piva,et al. Empirical Generalization Study: Unsupervised Domain Adaptation vs. Domain Generalization Methods for Semantic Segmentation in the Wild , 2023, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
[6] A. Khoreva,et al. Intra-Source Style Augmentation for Improved Domain Generalization , 2022, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
[7] Conghui Hu,et al. Feature Representation Learning for Unsupervised Cross-domain Image Retrieval , 2022, ECCV.
[8] Gim Hee Lee,et al. Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation , 2022, NeurIPS.
[9] Lili Yao,et al. DIRL: Domain-Invariant Representation Learning for Generalizable Semantic Segmentation , 2022, AAAI.
[10] Kilian Q. Weinberger,et al. Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Maxwell D. Collins,et al. CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Wei-Ting Chen,et al. Learning Multiple Adverse Weather Removal via Two-stage Knowledge Learning and Multi-contrastive Regularization: Toward a Unified Model , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[13] H. Bischof,et al. An Efficient Domain-Incremental Learning Approach to Drive in All Weather Conditions , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[14] Dongbo Min,et al. Pin the Memory: Learning to Generalize Semantic Segmentation , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Gim Hee Lee,et al. Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation , 2022, ECCV.
[16] Euntai Kim,et al. WildNet: Learning Domain Generalized Semantic Segmentation from the Wild , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Yinjie Lei,et al. Semantic-Aware Domain Generalized Segmentation , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Pengfei Zhu,et al. Label-efficient Hybrid-supervised Learning for Medical Image Segmentation , 2022, AAAI.
[19] James Hays,et al. MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation , 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[20] A. Schwing,et al. Masked-attention Mask Transformer for Universal Image Segmentation , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Li Dong,et al. Swin Transformer V2: Scaling Up Capacity and Resolution , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Mahsa Baktash,et al. Learning to Diversify for Single Domain Generalization , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[23] Xi Peng,et al. Out-of-Domain Generalization From a Single Source: An Uncertainty Quantification Approach , 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[24] Lingqiao Liu,et al. Global and Local Texture Randomization for Synthetic-to-Real Semantic Segmentation , 2021, IEEE Transactions on Image Processing.
[25] Alexander G. Schwing,et al. Per-Pixel Classification is Not All You Need for Semantic Segmentation , 2021, NeurIPS.
[26] Ping Liu,et al. Adversarial Semantic Hallucination for Domain Generalized Semantic Segmentation , 2021, 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
[27] Qi Bi,et al. Learning Calibrated Medical Image Segmentation via Multi-rater Agreement Modeling , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Anima Anandkumar,et al. SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers , 2021, NeurIPS.
[29] Cordelia Schmid,et al. Segmenter: Transformer for Semantic Segmentation , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[30] Luc Van Gool,et al. ACDC: The Adverse Conditions Dataset with Correspondences for Semantic Driving Scene Understanding , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[31] Seungryong Kim,et al. RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[32] A. Yuille,et al. MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[33] S. Gelly,et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale , 2020, ICLR.
[34] Bin Li,et al. Deformable DETR: Deformable Transformers for End-to-End Object Detection , 2020, ICLR.
[35] Federico Tombari,et al. Batch Normalization Embeddings for Deep Domain Generalization , 2020, Pattern Recognit..
[36] Judy Hoffman,et al. Learning to Balance Specificity and Invariance for In and Out of Domain Generalization , 2020, ECCV.
[37] Lequan Yu,et al. Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization , 2020, ECCV.
[38] Timothy M. Hospedales,et al. Learning to Generate Novel Domains for Domain Generalization , 2020, ECCV.
[39] Eric P. Xing,et al. Self-Challenging Improves Cross-Domain Generalization , 2020, ECCV.
[40] Amit Sharma,et al. Domain Generalization using Causal Matching , 2020, ICML.
[41] Xi Peng,et al. Learning to Learn Single Domain Generalization , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[42] Tatsuya Harada,et al. Domain Generalization Using a Mixture of Multiple Latent Domains , 2019, AAAI.
[43] K. Keutzer,et al. Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization Without Accessing Target Domain Data , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[44] Yang Zhao,et al. Deep High-Resolution Representation Learning for Visual Recognition , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[45] Gurumurthy Swaminathan,et al. d-SNE: Domain Adaptation Using Stochastic Neighborhood Embedding , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[46] Xiaoou Tang,et al. Switchable Whitening for Deep Representation Learning , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[47] Lei Huang,et al. Iterative Normalization: Beyond Standardization Towards Efficient Whitening , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[48] Fabio Maria Carlucci,et al. Domain Generalization by Solving Jigsaw Puzzles , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[49] Jun Fu,et al. Dual Attention Network for Scene Segmentation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[50] Xiaoou Tang,et al. Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net , 2018, ECCV.
[51] Trevor Darrell,et al. BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling , 2018, ArXiv.
[52] Silvio Savarese,et al. Generalizing to Unseen Domains via Adversarial Data Augmentation , 2018, NeurIPS.
[53] George Papandreou,et al. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation , 2018, ECCV.
[54] Peter Kontschieder,et al. The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[55] Donald A. Adjeroh,et al. Unified Deep Supervised Domain Adaptation and Generalization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[56] Ian D. Reid,et al. RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[57] Vladlen Koltun,et al. Playing for Data: Ground Truth from Computer Games , 2016, ECCV.
[58] Antonio M. López,et al. The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[59] Sebastian Ramos,et al. The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[60] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[61] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[62] Fei-Fei Li,et al. ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[63] Maxwell D. Collins,et al. k-means Mask Transformer , 2022, ECCV.
[64] R. Giryes,et al. Supplementary Material for Unsupervised Domain Generalization by Learning a Bridge Across Domains , 2022 .
[65] Stephen Lin,et al. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[66] Hongseok Namkoong,et al. Evaluating model performance under worst-case subpopulations , 2021, NeurIPS.
[67] Tongliang Liu,et al. Domain Generalization via Entropy Regularization , 2020, NeurIPS.
[68] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.