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.πππππ
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.πππ
Vaswani, Ashish, et al. "Attention is all you need." Advances in neural information processing systems. 2017.ππππ
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.πππππ
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.ππππ
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. ππππ
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.ππππ
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.πππππ
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). πππππ
Roy, Abhijit Guha, Nassir Navab, and Christian Wachinger. "Concurrent spatial and channel βsqueeze & excitationβin fully convolutional networks." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2018. ππππ
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).πππ
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.πππ
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).πππ
Zhao, Hengshuang, et al. "Psanet: Point-wise spatial attention network for scene parsing." Proceedings of the European Conference on Computer Vision (ECCV). 2018.πππ
Pu, Shi, et al. "Deep attentive tracking via reciprocative learning." Advances in Neural Information Processing Systems. 2018.ππππ
Woo, Sanghyun, et al. "Cbam: Convolutional block attention module." Proceedings of the European Conference on Computer Vision (ECCV). 2018.ππππ
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.πππ
Yuan, Yuhui, and Jingdong Wang. "Ocnet: Object context network for scene parsing." arXiv preprint arXiv:1809.00916 (2018).πππ
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.πππ
CARAFE: Content-Aware ReAssembly of Features
Li, Xia, et al. "Expectation-Maximization Attention Networks for Semantic Segmentation." arXiv preprint arXiv:1907.13426 (2019). ππππ
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 οΌ2020οΌ
Mixed High-Order Attention Network for Person Re-Identification οΌICCV 2019οΌ
Fine-grained Recognition: Accounting for Subtle Differences between Similar Classes οΌAAAI 2020οΌ
Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations οΌCVPR 2019οΌ
Co-saliency Detection via Mask-guided Fully Convolutional Networks with Multi-scale Label Smoothing (CVPR 2019)
Feature Weighting and Boosting for Few-Shot Segmentation οΌICCV 2019οΌ
Domain adaptation
Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014). ππππ
Bousmalis, Konstantinos, et al. "Domain separation networks." Advances in neural information processing systems. 2016. πππππ
Tzeng, Eric, et al. "Adversarial discriminative domain adaptation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017. ππππ
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. ππππ
Murez, Zak, et al. "Image to image translation for domain adaptation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. ππππ
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. ππππ
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. ππππ
Vu, Tuan-Hung, et al. "DADA: Depth-aware Domain Adaptation in Semantic Segmentation." arXiv preprint arXiv:1904.01886 (2019). πππ
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. ππππ
Zhang, Yiheng, et al. "Fully convolutional adaptation networks for semantic segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. ππππ
Xu, Xiang, et al. "d-SNE: Domain Adaptation using Stochastic Neighborhood Embedding." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019. πππ
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. ππππ
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. ππππ
Chen, Chaoqi, et al. "Progressive Feature Alignment for Unsupervised Domain Adaptation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019. πππππ
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.πππππ
Zhu, Xinge, et al. "Adapting Object Detectors via Selective Cross-Domain Alignment." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.πππ
Gong, Rui, et al. "DLOW: Domain flow for adaptation and generalization." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.ππππ
Saito, Kuniaki, et al. "Maximum classifier discrepancy for unsupervised domain adaptation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. πππππ
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. πππ
Lee, Chen-Yu, et al. "Sliced wasserstein discrepancy for unsupervised domain adaptation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019. ππππ
Pan, Yingwei, et al. "Transferrable Prototypical Networks for Unsupervised Domain Adaptation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019. ππππ
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. ππππ
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. ππππ
Zou, Yang, et al. "Confidence Regularized Self-Training." Proceedings of the IEEE international conference on computer vision. 2019. ππππ
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. πππ
Raghu, Maithra, et al. "Transfusion: Understanding transfer learning with applications to medical imaging." arXiv preprint arXiv:1902.07208 (2019). πππππ
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. ππππ 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. πππππ 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). ππππ
Yang, Maoke, et al. "Denseaspp for semantic segmentation in street scenes." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. ππππ
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. ππππ
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. πππ
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. πππππ
Li, Xiaomeng, et al. "Semi-supervised Skin Lesion Segmentation via Transformation Consistent Self-ensembling Model." arXiv preprint arXiv:1808.03887 (2018). πππ
Zhang, Ying, et al. "Deep mutual learning." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. πππππ
Xie, Yutong, et al. "Semi-and Weakly Supervised Directional Bootstrapping Model for Automated Skin Lesion Segmentation." arXiv preprint arXiv:1903.03313 (2019). πππ
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. ππππ
Muhammad H, Sigel C S, Campanella G, et al. Unsupervised Subtyping of Cholangiocarcinoma Using a Deep Clustering Convolutional Autoencoder[C]//MICCAI 2019: 604-612. πππ
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. ππ
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. πππ
Chen S, Bortsova G, JuΓ‘rez A G U, et al. Multi-task Attention-Based Semi-supervised Learning for Medical Image Segmentation[C]//MICCAI 2019: 457-465. ππ
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. πππ
Bortsova G, Dubost F, Hogeweg L, et al. Semi-supervised Medical Image Segmentation via Learning Consistency Under Transformations[C]//MICCAI 2019: 810-818. ππ
Kervadec H, Dolz J, Granger E, et al. Curriculum semi-supervised segmentation[J]. arXiv preprint arXiv:1904.05236, 2019. πππ
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. πππ
Invariant Information Clustering for Unsupervised Image Classification and Segmentation. (ICCV 2019) ππππ
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. πππ
Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation. (ICCV 2019) ππππ
Normalized Wasserstein for Mixture Distributions with Applications in Adversarial Learning and Domain Adaptation. (ICCV 2019) ππππ
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. ππππ
Xu M, Zhang J, Ni B, et al. Adversarial Domain Adaptation with Domain Mixup[J]. AAAI 2020 oral. ππππ
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. ππππ
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. ππ
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. ππ
Eaton-Rosen Z, Bragman F, Ourselin S, et al. Improving data augmentation for medical image segmentation[J]. 2018. ππ
DeVries T, Taylor G W. Improved regularization of convolutional neural networks with cutout[J]. arXiv preprint arXiv:1708.04552, 2017. ππ
French G, Aila T, Laine S, et al. SEMI-SUPERVISED SEMANTIC SEGMENTATION NEEDS STRONG, HIGH-DIMENSIONAL PERTURBATIONS[J]. ππ
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. πππ
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. πππ
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. ππππ
Lim S, Kim I, Kim T, et al. Fast autoaugment[C]//Advances in Neural Information Processing Systems. 2019: 6662-6672. ππππ
Ho D, Liang E, Stoica I, et al. Population based augmentation: Efficient learning of augmentation policy schedules[J]. arXiv preprint arXiv:1905.05393, 2019. ππππ
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 οΌ2020οΌ
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, AΓ―cha, 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. πππ
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. πππ
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. ππππ
Su, Jinming, et al. "Selectivity or Invariance: Boundary-aware Salient Object Detection." arXiv preprint arXiv:1812.10066 (2018).πππ
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. πππ
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. πππππ
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. πππ
Novotny, David, et al. "Semi-convolutional operators for instance segmentation." Proceedings of the European Conference on Computer Vision (ECCV). 2018. ππππ
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). πππ
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). ππππ
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).ππππ
Tofighi, Mohammad, et al. "Prior Information Guided Regularized Deep Learning for Cell Nucleus Detection." IEEE transactions on medical imaging (2019). ππππ
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. ππππ
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. πππ
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. πππ
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. π
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. πππ
Painchaud N, Skandarani Y, Judge T, et al. Cardiac MRI Segmentation with Strong Anatomical Guarantees[C]//MICCAI 2019: 632-640. ππππ
Multi-class Part Parsing with Joint Boundary-Semantic Awareness (ICCV 2019)
Liu, Weiyang, et al. "Large-margin softmax loss for convolutional neural networks." ICML. Vol. 2. No. 3. 2016. ππππ
Sphereface : Liu, Weiyang, et al. "Sphereface: Deep hypersphere embedding for face recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. ππππ
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. ππππ
Wang, Jian, et al. "Deep metric learning with angular loss." Proceedings of the IEEE International Conference on Computer Vision. 2017. ππππ
Lin, Tsung-Yi, et al. "Focal loss for dense object detection." Proceedings of the IEEE international conference on computer vision. 2017. πππππ
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. πππ
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.πππ
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. πππππ
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). πππππ
Wang, Feng, et al. "Additive margin softmax for face verification." IEEE Signal Processing Letters 25.7 (2018): 926-930. ππππ
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. πππππ
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. ππππ
Sun, Ming, et al. "Multi-attention multi-class constraint for fine-grained image recognition." Proceedings of the European Conference on Computer Vision (ECCV). 2018. ππππ
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. ππππ
Zhu, Xinge, et al. "Penalizing top performers: Conservative loss for semantic segmentation adaptation." Proceedings of the European Conference on Computer Vision (ECCV). 2018.πππ
Hayat, Munawar, et al. "Max-margin Class Imbalanced Learning with Gaussian Affinity." arXiv preprint arXiv:1901.07711 (2019). πππ
Lifchitz, Yann, et al. "Dense Classification and Implanting for Few-Shot Learning." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019. ππππ
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. πππ
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. ππππ
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. πππππ
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. ππππ
Fine-grained Recognition: Accounting for Subtle Differences between Similar Classes οΌAAAI 2020οΌπππ
Bag of Tricks for Image Classification with Convolutional Neural Networks (CVPR 2019) ππππ
When Does Label Smoothing Help? (NeurIPS 2019)
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Reading list for deep learning in Computer Vision and Medical Image Analysis