Reading List

• The goal of this document is to provide a reading list for Deep Learning in Computer Vision and Medical Image Analysis Field.

Attention & Saliency

• Zhou, Bolei, et al. "Learning deep features for discriminative localization." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.👍👍👍👍👍
• Chen, Liang-Chieh, et al. "Attention to scale: Scale-aware semantic image segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.👍👍👍
• Vaswani, Ashish, et al. "Attention is all you need." Advances in neural information processing systems. 2017.👍👍👍👍
• Selvaraju, Ramprasaath R., et al. "Grad-cam: Visual explanations from deep networks via gradient-based localization." Proceedings of the IEEE International Conference on Computer Vision. 2017.👍👍👍👍👍
• Wang, Fei, et al. "Residual attention network for image classification." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017.
• Zhang, Rui, et al. "Scale-adaptive convolutions for scene parsing." Proceedings of the IEEE International Conference on Computer Vision. 2017.👍👍👍👍
• Fu, Jianlong, Heliang Zheng, and Tao Mei. "Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. 👍👍👍👍
• Wei, Zhen, et al. "Learning adaptive receptive fields for deep image parsing network." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017.👍👍👍👍
• Mahapatra, Dwarikanath, et al. "Image super resolution using generative adversarial networks and local saliency maps for retinal image analysis." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2017.
• Guan, Qingji, and Yaping Huang. "Multi-label chest X-ray image classification via category-wise residual attention learning." Pattern Recognition Letters (2018).
• Wang, Xiaolong, et al. "Non-local neural networks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.👍👍👍👍👍
• Zhang, Han, et al. "Self-attention generative adversarial networks." arXiv preprint arXiv:1805.08318 (2018). 👍👍👍👍(Tensorflow-code)
• Yan, Yichao, et al. "Multi-level attention model for person re-identification." Pattern Recognition Letters (2018).
• SENet: Hu, Jie, Li Shen, and Gang Sun. "Squeeze-and-excitation networks."In CVPR (2018). 👍👍👍👍👍
• Roy, Abhijit Guha, Nassir Navab, and Christian Wachinger. "Concurrent spatial and channel ‘squeeze & excitation’in fully convolutional networks." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2018. 👍👍👍👍
• Xu, Rudong, et al. "Attention-Mechanism-Containing Neural Networks for High-Resolution Remote Sensing Image Classification." Remote Sensing 10.10 (2018): 1602.
• Guan, Qingji, et al. "Diagnose like a radiologist: Attention guided convolutional neural network for thorax disease classification." arXiv preprint arXiv:1801.09927 (2018).
• Sarafianos, Nikolaos, Xiang Xu, and Ioannis A. Kakadiaris. "Deep imbalanced attribute classification using visual attention aggregation." Proceedings of the European Conference on Computer Vision (ECCV). 2018.
• Oktay, Ozan, et al. "Attention u-net: Learning where to look for the pancreas." arXiv preprint arXiv:1804.03999 (2018).
• Wang, Yi, et al. "Deep attentional features for prostate segmentation in ultrasound." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2018.
• Li, Hanchao, et al. "Pyramid attention network for semantic segmentation." arXiv preprint arXiv:1805.10180 (2018).
• AffinityNet: Ahn, Jiwoon, and Suha Kwak. "Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.
• Ke, Zi-Yi, and Chiou-Ting Hsu. "Generating Self-Guided Dense Annotations for Weakly Supervised Semantic Segmentation." arXiv preprint arXiv:1810.07050 (2018).👍👍👍
• Li, Kunpeng, et al. "Tell me where to look: Guided attention inference network." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.👍👍👍
• Fan, Lei, et al. "Semantic segmentation with global encoding and dilated decoder in street scenes." IEEE Access 6 (2018): 50333-50343.
• Huang, Zilong, et al. "Ccnet: Criss-cross attention for semantic segmentation." arXiv preprint arXiv:1811.11721 (2018).👍👍👍
• Zhao, Hengshuang, et al. "Psanet: Point-wise spatial attention network for scene parsing." Proceedings of the European Conference on Computer Vision (ECCV). 2018.👍👍👍
• Pu, Shi, et al. "Deep attentive tracking via reciprocative learning." Advances in Neural Information Processing Systems. 2018.👍👍👍👍
• Woo, Sanghyun, et al. "Cbam: Convolutional block attention module." Proceedings of the European Conference on Computer Vision (ECCV). 2018.👍👍👍👍
• Jiao, Jianbo, et al. "Look deeper into depth: Monocular depth estimation with semantic booster and attention-driven loss." Proceedings of the European Conference on Computer Vision (ECCV). 2018.👍👍👍
• Yuan, Yuhui, and Jingdong Wang. "Ocnet: Object context network for scene parsing." arXiv preprint arXiv:1809.00916 (2018).👍👍👍
• Ling, Hefei, et al. "Self Residual Attention Network for Deep Face Recognition." IEEE Access 7 (2019): 55159-55168.
• Fu, Jun, et al. "Dual attention network for scene segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.👍👍👍
• CARAFE: Content-Aware ReAssembly of Features
• Li, Xia, et al. "Expectation-Maximization Attention Networks for Semantic Segmentation." arXiv preprint arXiv:1907.13426 (2019). 👍👍👍👍
• Ding B, Long C, Zhang L, et al. ARGAN: attentive recurrent generative adversarial network for shadow detection and removal[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 10213-10222.
• Longformer: The Long-Document Transformer （2020）
• Mixed High-Order Attention Network for Person Re-Identification （ICCV 2019）
• Fine-grained Recognition: Accounting for Subtle Differences between Similar Classes （AAAI 2020）
• Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations （CVPR 2019）
• Co-saliency Detection via Mask-guided Fully Convolutional Networks with Multi-scale Label Smoothing (CVPR 2019)
• Feature Weighting and Boosting for Few-Shot Segmentation （ICCV 2019）

Domain adaptation

• Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014). 👍👍👍👍
• Bousmalis, Konstantinos, et al. "Domain separation networks." Advances in neural information processing systems. 2016. 👍👍👍👍👍
• Tzeng, Eric, et al. "Adversarial discriminative domain adaptation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017. 👍👍👍👍
• Bousmalis, Konstantinos, et al. "Unsupervised pixel-level domain adaptation with generative adversarial networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. 👍👍👍👍
• Murez, Zak, et al. "Image to image translation for domain adaptation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. 👍👍👍👍
• Tsai, Yi-Hsuan, et al. "Learning to adapt structured output space for semantic segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. 👍👍👍👍
• Vu, Tuan-Hung, et al. "Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019. 👍👍👍👍
• Vu, Tuan-Hung, et al. "DADA: Depth-aware Domain Adaptation in Semantic Segmentation." arXiv preprint arXiv:1904.01886 (2019). 👍👍👍
• Chen, Minghao, Hongyang Xue, and Deng Cai. "Domain Adaptation for Semantic Segmentation with Maximum Squares Loss." Proceedings of the IEEE International Conference on Computer Vision. 2019. 👍👍👍👍
• Zhang, Yiheng, et al. "Fully convolutional adaptation networks for semantic segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. 👍👍👍👍
• Xu, Xiang, et al. "d-SNE: Domain Adaptation using Stochastic Neighborhood Embedding." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019. 👍👍👍
• Chang, Wei-Lun, et al. "All about Structure: Adapting Structural Information across Domains for Boosting Semantic Segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019. 👍👍👍👍
• Chen, Yun-Chun, et al. "CrDoCo: Pixel-Level Domain Transfer With Cross-Domain Consistency." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019. 👍👍👍👍
• Chen, Chaoqi, et al. "Progressive Feature Alignment for Unsupervised Domain Adaptation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019. 👍👍👍👍👍
• You, Kaichao, et al. "Universal Domain Adaptation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.
• Kang, Guoliang, et al. "Contrastive Adaptation Network for Unsupervised Domain Adaptation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.👍👍👍👍👍
• Zhu, Xinge, et al. "Adapting Object Detectors via Selective Cross-Domain Alignment." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.👍👍👍
• Gong, Rui, et al. "DLOW: Domain flow for adaptation and generalization." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.👍👍👍👍
• Saito, Kuniaki, et al. "Maximum classifier discrepancy for unsupervised domain adaptation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. 👍👍👍👍👍
• Luo, Yawei, et al. "Taking a closer look at domain shift: Category-level adversaries for semantics consistent domain adaptation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019. 👍👍👍
• Lee, Chen-Yu, et al. "Sliced wasserstein discrepancy for unsupervised domain adaptation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019. 👍👍👍👍
• Pan, Yingwei, et al. "Transferrable Prototypical Networks for Unsupervised Domain Adaptation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019. 👍👍👍👍
• Li, Yunsheng, Lu Yuan, and Nuno Vasconcelos. "Bidirectional Learning for Domain Adaptation of Semantic Segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019. 👍👍👍👍
• Zou, Yang, et al. "Unsupervised domain adaptation for semantic segmentation via class-balanced self-training." Proceedings of the European Conference on Computer Vision (ECCV). 2018. 👍👍👍👍
• Zou, Yang, et al. "Confidence Regularized Self-Training." Proceedings of the IEEE international conference on computer vision. 2019. 👍👍👍👍
• Chen, Yuhua, et al. "Learning semantic segmentation from synthetic data: A geometrically guided input-output adaptation approach." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019. 👍👍👍
• Raghu, Maithra, et al. "Transfusion: Understanding transfer learning with applications to medical imaging." arXiv preprint arXiv:1902.07208 (2019). 👍👍👍👍👍

Segmentation

• Deeplab: Chen, Liang-Chieh, et al. "Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs." IEEE transactions on pattern analysis and machine intelligence 40.4 (2017): 834-848. 👍👍👍👍 Tensorflow-code
• Deeplab v3+: Chen, Liang-Chieh, et al. "Encoder-decoder with atrous separable convolution for semantic image segmentation." Proceedings of the European conference on computer vision (ECCV). 2018. 👍👍👍👍👍 Tensorflow-code
• Tokunaga H, Teramoto Y, Yoshizawa A, et al. Adaptive Weighting Multi-Field-of-View CNN for Semantic Segmentation in Pathology[C]//Proceedings of the IEEE CVPR. 2019: 12597-12606.
• Takikawa, Towaki, et al. "Gated-scnn: Gated shape cnns for semantic segmentation." arXiv preprint arXiv:1907.05740 (2019). 👍👍👍👍
• Yang, Maoke, et al. "Denseaspp for semantic segmentation in street scenes." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. 👍👍👍👍
• Zhao S, Wang Y, Yang Z, et al. Region Mutual Information Loss for Semantic Segmentation[C]//Advances in Neural Information Processing Systems. 2019: 11115-11125. 👍👍👍👍

Semi-supervised & Unsupervised Learing

• Wei, Yunchao, et al. "Revisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. 👍👍👍
• Dalca, Adrian V., John Guttag, and Mert R. Sabuncu. "Anatomical priors in convolutional networks for unsupervised biomedical segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. 👍👍👍👍👍
• Li, Xiaomeng, et al. "Semi-supervised Skin Lesion Segmentation via Transformation Consistent Self-ensembling Model." arXiv preprint arXiv:1808.03887 (2018). 👍👍👍
• Zhang, Ying, et al. "Deep mutual learning." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. 👍👍👍👍👍
• Xie, Yutong, et al. "Semi-and Weakly Supervised Directional Bootstrapping Model for Automated Skin Lesion Segmentation." arXiv preprint arXiv:1903.03313 (2019). 👍👍👍
• Wu, Si, et al. "Mutual Learning of Complementary Networks via Residual Correction for Improving Semi-Supervised Classification." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019. 👍👍👍👍
• Muhammad H, Sigel C S, Campanella G, et al. Unsupervised Subtyping of Cholangiocarcinoma Using a Deep Clustering Convolutional Autoencoder[C]//MICCAI 2019: 604-612. 👍👍👍
• Xue Y, Zhou Q, Ye J, et al. Synthetic Augmentation and Feature-Based Filtering for Improved Cervical Histopathology Image Classification[C]//MICCAI 2019: 387-396. 👍👍
• Yu L, Wang S, Li X, et al. Uncertainty-Aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation[C]//MICCAI 2019: 605-613. 👍👍👍
• Chen S, Bortsova G, Juárez A G U, et al. Multi-task Attention-Based Semi-supervised Learning for Medical Image Segmentation[C]//MICCAI 2019: 457-465. 👍👍
• Zheng H, Lin L, Hu H, et al. Semi-supervised Segmentation of Liver Using Adversarial Learning with Deep Atlas Prior[C]//MICCAI 2019: 148-156. 👍👍👍
• Bortsova G, Dubost F, Hogeweg L, et al. Semi-supervised Medical Image Segmentation via Learning Consistency Under Transformations[C]//MICCAI 2019: 810-818. 👍👍
• Kervadec H, Dolz J, Granger E, et al. Curriculum semi-supervised segmentation[J]. arXiv preprint arXiv:1904.05236, 2019. 👍👍👍
• Yang J, Dvornek N C, Zhang F, et al. Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation[C]//MICCAI 2019: 255-263. 👍👍👍
• S4L: Self-Supervised Semi-Supervised Learning. (ICCV 2019) 👍👍👍
• Invariant Information Clustering for Unsupervised Image Classification and Segmentation. (ICCV 2019) 👍👍👍👍
• Qi G J, Zhang L, Chen C W, et al. AVT: Unsupervised Learning of Transformation Equivariant Representations by Autoencoding Variational Transformations[J]. arXiv preprint arXiv:1903.10863, 2019. 👍👍👍
• Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation. (ICCV 2019) 👍👍👍👍
• Normalized Wasserstein for Mixture Distributions with Applications in Adversarial Learning and Domain Adaptation. (ICCV 2019) 👍👍👍👍

Data Augmentation

• Wang Q, Li W, Gool L V. Semi-supervised learning by augmented distribution alignment[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 1466-1475. 👍👍👍👍
• Xu M, Zhang J, Ni B, et al. Adversarial Domain Adaptation with Domain Mixup[J]. AAAI 2020 oral. 👍👍👍👍
• Li Z, Kamnitsas K, Glocker B. Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019: 402-410. 👍👍👍👍
• Chaitanya K, Karani N, Baumgartner C F, et al. Semi-supervised and task-driven data augmentation[C]//International Conference on Information Processing in Medical Imaging. Springer, Cham, 2019: 29-41. 👍👍
• Panfilov E, Tiulpin A, Klein S, et al. Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops. 2019: 0-0. 👍👍
• Eaton-Rosen Z, Bragman F, Ourselin S, et al. Improving data augmentation for medical image segmentation[J]. 2018. 👍👍
• DeVries T, Taylor G W. Improved regularization of convolutional neural networks with cutout[J]. arXiv preprint arXiv:1708.04552, 2017. 👍👍
• French G, Aila T, Laine S, et al. SEMI-SUPERVISED SEMANTIC SEGMENTATION NEEDS STRONG, HIGH-DIMENSIONAL PERTURBATIONS[J]. 👍👍
• Yun S, Han D, Oh S J, et al. Cutmix: Regularization strategy to train strong classifiers with localizable features[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 6023-6032. 👍👍👍
• Hendrycks D, Mu N, Cubuk E D, et al. AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty[J]. arXiv preprint arXiv:1912.02781, 2019. 👍👍👍
• Choi J, Kim T, Kim C. Self-Ensembling with GAN-based Data Augmentation for Domain Adaptation in Semantic Segmentation[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 6830-6840.
• Zhang K, Li T, Liu B, et al. Co-saliency detection via mask-guided fully convolutional networks with multi-scale label smoothing[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 3095-3104.
• Beckham C, Honari S, Verma V, et al. On Adversarial Mixup Resynthesis[C]//Advances in Neural Information Processing Systems. 2019: 4348-4359.
• Cubuk E D, Zoph B, Mane D, et al. Autoaugment: Learning augmentation strategies from data[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2019: 113-123. 👍👍👍👍
• Lim S, Kim I, Kim T, et al. Fast autoaugment[C]//Advances in Neural Information Processing Systems. 2019: 6662-6672. 👍👍👍👍
• Ho D, Liang E, Stoica I, et al. Population based augmentation: Efficient learning of augmentation policy schedules[J]. arXiv preprint arXiv:1905.05393, 2019. 👍👍👍👍
• mixup: BEYOND EMPIRICAL RISK MINIMIZATION. ICLR 2018👍👍👍👍👍
• MixMatch: A Holistic Approach to Semi-Supervised Learning (NeurIPS 2019)
• FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence (2020)
• SuperMix: Supervising the Mixing Data Augmentation （2020）
• Interpolation Consistency Training for Semi-Supervised Learning (IJCAI 2019)
• Virtual adversarial training: a regularization method for supervised and semi-supervised learning

Prior Information (edge, shape, anatomical)

• BenTaieb, Aïcha, and Ghassan Hamarneh. "Topology aware fully convolutional networks for histology gland segmentation." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2016. 👍👍👍
• Ravishankar, Hariharan, et al. "Learning and incorporating shape models for semantic segmentation." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2017. 👍👍👍
• Oktay, Ozan, et al. "Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation." IEEE transactions on medical imaging 37.2 (2017): 384-395. 👍👍👍👍
• Su, Jinming, et al. "Selectivity or Invariance: Boundary-aware Salient Object Detection." arXiv preprint arXiv:1812.10066 (2018).👍👍👍
• Duan, Jinming, et al. "Combining Deep Learning and Shape Priors for Bi-Ventricular Segmentation of Volumetric Cardiac Magnetic Resonance Images." International Workshop on Shape in Medical Imaging. Springer, Cham, 2018. 👍👍👍
• Pumarola, Albert, et al. "Geometry-aware network for non-rigid shape prediction from a single view." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.
• Dalca, Adrian V., John Guttag, and Mert R. Sabuncu. "Anatomical priors in convolutional networks for unsupervised biomedical segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. 👍👍👍👍👍
• Mirikharaji, Zahra, and Ghassan Hamarneh. "Star shape prior in fully convolutional networks for skin lesion segmentation." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2018. 👍👍👍
• Novotny, David, et al. "Semi-convolutional operators for instance segmentation." Proceedings of the European Conference on Computer Vision (ECCV). 2018. 👍👍👍👍
• Liu, Rosanne, et al. "An intriguing failing of convolutional neural networks and the coordconv solution." Advances in Neural Information Processing Systems. 2018.
• Kulikov, Victor, and Victor Lempitsky. "Instance Segmentation of Biological Images Using Harmonic Embeddings." arXiv preprint arXiv:1904.05257 (2019). 👍👍👍
• Duan, Jinming, et al. "Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach." IEEE transactions on medical imaging (2019). 👍👍👍👍
• Zhou, Yuyin, et al. "Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation." arXiv preprint arXiv:1904.06346 (2019).
• Ea-GANs: Yu, Biting, et al. "Ea-GANs: Edge-aware Generative Adversarial Networks for Cross-modality MR Image Synthesis." IEEE transactions on medical imaging (2019).👍👍👍👍
• Tofighi, Mohammad, et al. "Prior Information Guided Regularized Deep Learning for Cell Nucleus Detection." IEEE transactions on medical imaging (2019). 👍👍👍👍
• Neven, Davy, et al. "Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019. 👍👍👍👍
• Mukundan, Arun, Giorgos Tolias, and Ondrej Chum. "Explicit Spatial Encoding for Deep Local Descriptors." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019. 👍👍👍
• Ulyanov D, Vedaldi A, Lempitsky V. Deep image prior[C]//Proceedings of the IEEE CVPR. 2018: 9446-9454.
• Chen C, Biffi C, Tarroni G, et al. Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images[C]//MICCAI 2019: 523-531. 👍👍👍
• Larrazabal A J, Martinez C, Ferrante E. Anatomical Priors for Image Segmentation via Post-Processing with Denoising Autoencoders[J]. arXiv preprint arXiv:1906.02343, 2019. 👍
• Yue Q, Luo X, Ye Q, et al. Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors[J]. arXiv preprint arXiv:1906.07347, 2019. 👍👍👍
• Painchaud N, Skandarani Y, Judge T, et al. Cardiac MRI Segmentation with Strong Anatomical Guarantees[C]//MICCAI 2019: 632-640. 👍👍👍👍
• Multi-class Part Parsing with Joint Boundary-Semantic Awareness (ICCV 2019)
• Stacked Cross Refinement Network for Edge-Aware Salient Object Detection (ICCV 2019)
• Salient Object Detection with Purificatory Mechanism and Structural Similarity Loss (2019)
• TransGaGa: Geometry-Aware Unsupervised Image-to-Image Translation (CVPR 2019)

Loss function

• Liu, Weiyang, et al. "Large-margin softmax loss for convolutional neural networks." ICML. Vol. 2. No. 3. 2016. 👍👍👍👍
• Sphereface : Liu, Weiyang, et al. "Sphereface: Deep hypersphere embedding for face recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. 👍👍👍👍
• Talmi, Itamar, Roey Mechrez, and Lihi Zelnik-Manor. "Template matching with deformable diversity similarity." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017. 👍👍👍👍
• Wang, Jian, et al. "Deep metric learning with angular loss." Proceedings of the IEEE International Conference on Computer Vision. 2017. 👍👍👍👍
• Lin, Tsung-Yi, et al. "Focal loss for dense object detection." Proceedings of the IEEE international conference on computer vision. 2017. 👍👍👍👍👍
• Wong, Ken CL, et al. "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2018. 👍👍👍
• Chen, Bike, Chen Gong, and Jian Yang. "Importance-aware semantic segmentation for autonomous vehicles." IEEE Transactions on Intelligent Transportation Systems 20.1 (2018): 137-148.👍👍👍
• Yu, Xin, et al. "Face super-resolution guided by facial component heatmaps." Proceedings of the European Conference on Computer Vision (ECCV). 2018.
• Liu, Weiyang, et al. "Decoupled networks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. 👍👍👍👍👍
• AutoLoss: Xu, Haowen, et al. "AutoLoss: Learning Discrete Schedules for Alternate Optimization." arXiv preprint arXiv:1810.02442 (2018).
• Mechrez, Roey, Itamar Talmi, and Lihi Zelnik-Manor. "The contextual loss for image transformation with non-aligned data." Proceedings of the European Conference on Computer Vision (ECCV). 2018.
• Wang, Xiaobo, et al. "Support Vector Guided Softmax Loss for Face Recognition." arXiv preprint arXiv:1812.11317 (2018). 👍👍👍👍👍
• Wang, Feng, et al. "Additive margin softmax for face verification." IEEE Signal Processing Letters 25.7 (2018): 926-930. 👍👍👍👍
• GM loss: Wan, Weitao, et al. "Rethinking feature distribution for loss functions in image classification." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. 👍👍👍👍👍
• Zhang, Xuaner, Ren Ng, and Qifeng Chen. "Single image reflection separation with perceptual losses." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. 👍👍👍👍
• Sun, Ming, et al. "Multi-attention multi-class constraint for fine-grained image recognition." Proceedings of the European Conference on Computer Vision (ECCV). 2018. 👍👍👍👍
• Wang, Cheng, et al. "Mancs: A multi-task attentional network with curriculum sampling for person re-identification." Proceedings of the European Conference on Computer Vision (ECCV). 2018.
• Liu, Weiyang, et al. "Learning towards minimum hyperspherical energy." Advances in Neural Information Processing Systems. 2018. 👍👍👍👍
• Zhu, Xinge, et al. "Penalizing top performers: Conservative loss for semantic segmentation adaptation." Proceedings of the European Conference on Computer Vision (ECCV). 2018.👍👍👍
• Hayat, Munawar, et al. "Max-margin Class Imbalanced Learning with Gaussian Affinity." arXiv preprint arXiv:1901.07711 (2019). 👍👍👍
• Lifchitz, Yann, et al. "Dense Classification and Implanting for Few-Shot Learning." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019. 👍👍👍👍
• CIA-Net: Zhou, Yanning, et al. "CIA-Net: Robust Nuclei Instance Segmentation with Contour-Aware Information Aggregation." International Conference on Information Processing in Medical Imaging. Springer, Cham, 2019. 👍👍👍
• Cui, Yin, et al. "Class-balanced loss based on effective number of samples." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.
• Wang, Xinshao, et al. "Deep metric learning by online soft mining and class-aware attention." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33. 2019. 👍👍👍👍
• Hui, Le, et al. "Inter-Class Angular Loss for Convolutional Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence. (2019).
• Arcface: Deng, Jiankang, et al. "Arcface: Additive angular margin loss for deep face recognition." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019. 👍👍👍👍👍
• Fair Loss: Liu B, Deng W, Zhong Y, et al. Fair Loss: Margin-Aware Reinforcement Learning for Deep Face Recognition[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 10052-10061. 👍👍👍👍
• Fine-grained Recognition: Accounting for Subtle Differences between Similar Classes （AAAI 2020）👍👍👍
• Bag of Tricks for Image Classification with Convolutional Neural Networks (CVPR 2019) 👍👍👍👍
• When Does Label Smoothing Help? (NeurIPS 2019)