|
从事计算机视觉领域的工作或者研究，必不可少的一件事情就是读论文，目前论文这么多，对于新手朋友们来说，该如何进行选读呢？本次来汇总一下我们的论文推荐专栏，给大家推荐各个方向，超过100篇文章，大家可以点击文末的‘往期链接’进入相关链接获取详细介绍。 作者&编辑 | 言有三 1. 初入深度学习领域 当我们初入这一行的时候，需要对基本的理论有比较好的理解，为之后的学习打下基础，这里我们从模型到数据给大家推荐了一些必读的文章。 1.1 初入深度学习CV领域必读的几篇文章 [1] Hubel D H, Wiesel T N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex[J]. The Journal of physiology, 1962, 160(1): 106-154. [2] Fukushima K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position[J]. Biological cybernetics, 1980, 36(4): 193-202. [3] LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324. [4] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. science, 2006, 313(5786): 504-507. [5] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems. 2012: 1097-1105. [6] Zeiler M D, Fergus R. Visualizing and understanding convolutional networks[C]//European conference on computer vision. Springer, Cham, 2014: 818-833. [7] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014. [8] Lin M, Chen Q, Yan S. Network in network[J]. arXiv preprint arXiv:1312.4400, 2013. [9] Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 1-9. [10] Waibel A, Hanazawa T, Hinton G, et al. Phoneme recognition using time-delay neural networks[J]. Backpropagation: Theory, Architectures and Applications, 1995: 35-61. [11] Ciresan D C, Meier U, Masci J, et al. Flexible, high performance convolutional neural networks for image classification[C]//Twenty-Second International Joint Conference on Artificial Intelligence. 2011. 1.2【每周CV论文推荐】 初学者必须精读的5篇深度学习优化相关文章 [1] Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks[C]//Proceedings of the thirteenth international conference on artificial intelligence and statistics. 2010: 249-256. [2] Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks[C]//Proceedings of the fourteenth international conference on artificial intelligence and statistics. 2011: 315-323. [3] Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. The journal of machine learning research, 2014, 15(1): 1929-1958. [4] Springenberg J T, Dosovitskiy A, Brox T, et al. Striving for simplicity: The all convolutional net[J]. arXiv preprint arXiv:1412.6806, 2014. [5] Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[J]. arXiv preprint arXiv:1502.03167, 2015. 1.3【每周CV论文推荐】 CV领域中数据增强相关的论文推荐 [1] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems. 2012: 1097-1105. [2] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014. [3] Chawla N V, Bowyer K W, Hall L O, et al. SMOTE: synthetic minority over-sampling technique[J]. Journal of artificial intelligence research, 2002, 16: 321-357. [4] Zhang H, Cisse M, Dauphin Y N, et al. mixup: Beyond empirical risk minimization[J]. arXiv preprint arXiv:1710.09412, 2017. [5] Cubuk E D, Zoph B, Mane D, et al. AutoAugment: Learning Augmentation Policies from Data.[J]. arXiv: Computer Vision and Pattern Recognition, 2018. 1.4 【每周CV论文推荐】 掌握残差网络必读的10多篇文章 [1] Schraudolph N. Accelerated gradient descent by factor-centering decomposition[J]. Technical report/IDSIA, 1998, 98. [2] Raiko T, Valpola H, LeCun Y. Deep learning made easier by linear transformations in perceptrons[C]//Artificial intelligence and statistics. 2012: 924-932. [3] Srivastava R K, Greff K, Schmidhuber J. Training very deep networks[C]//Advances in neural information processing systems. 2015: 2377-2385. [4] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778. [5] Veit A, Wilber M J, Belongie S. Residual networks behave like ensembles of relatively shallow networks[C]//Advances in neural information processing systems. 2016: 550-558. [6] Huang G, Sun Y, Liu Z, et al. Deep networks with stochastic depth[C]//European conference on computer vision. Springer, Cham, 2016: 646-661. [7] Orhan A E, Pitkow X. Skip connections eliminate singularities[J]. arXiv preprint arXiv:1701.09175, 2017. [8] He K, Zhang X, Ren S, et al. Identity mappings in deep residual networks[C]//European conference on computer vision. Springer, Cham, 2016: 630-645. [9] Zagoruyko S, Komodakis N. Wide residual networks[J]. arXiv preprint arXiv:1605.07146, 2016. [10] Xie S, Girshick R, Dollár P, et al. Aggregated residual transformations for deep neural networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 1492-1500. [11] Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4700-4708. [12] Yang Y, Zhong Z, Shen T, et al. Convolutional neural networks with alternately updated clique[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 2413-2422. 1.5【每周CV论文推荐】 初学高效率CNN模型设计应该读的文章 [1] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014. [2] Lin M, Chen Q, Yan S. Network in network[J]. arXiv preprint arXiv:1312.4400, 2013. [3] Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 1-9. [4] Iandola F N, Han S, Moskewicz M W, et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size[J]. arXiv preprint arXiv:1602.07360, 2016. [5] Chollet F. Xception: Deep learning with depthwise separable convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 1251-1258.. [6] Howard A G, Zhu M, Chen B, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications[J]. arXiv preprint arXiv:1704.04861, 2017. [7] Xie S, Girshick R, Dollár P, et al. Aggregated residual transformations for deep neural networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 1492-1500. [8] Shang W, Sohn K, Almeida D, et al. Understanding and improving convolutional neural networks via concatenated rectified linear units[C]//international conference on machine learning. 2016: 2217-2225. [9] Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4700-4708. 2. 深度学习CV基础领域 基于深度学习的发展是从一些基础领域开始的，包括图像分类，分割，检测，GAN等，所以我们肯定要学习相关的内容。 2.1【每周CV论文推荐】 初学目标检测必须要读的文章 [1] Sermanet P, Eigen D, Zhang X, et al. Overfeat: Integrated recognition, localization and detection using convolutional networks[J]. arXiv preprint arXiv:1312.6229, 2013. [2] Li H, Lin Z, Shen X, et al. A convolutional neural network cascade for face detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 5325-5334. [3] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2014: 580-587. [4] He K, Zhang X, Ren S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 37(9): 1904-1916. [5] Girshick R. Fast r-cnn[C]//Proceedings of the IEEE international conference on computer vision. 2015: 1440-1448. [6] Ren S, He K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks[C]//Advances in neural information processing systems. 2015: 91-99. [7] Dai J, Li Y, He K, et al. R-fcn: Object detection via region-based fully convolutional networks[C]//Advances in neural information processing systems. 2016: 379-387. [8] Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 2117-2125. 2.2【每周CV论文推荐】初学深度学习图像分割必须要读的文章 [1] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 3431-3440. [2] Badrinarayanan V, Kendall A, Cipolla R. Segnet: A deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(12): 2481-2495. [3] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[C]//International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015: 234-241. [4] Krähenbühl P, Koltun V. Efficient inference in fully connected crfs with gaussian edge potentials[C]//Advances in neural information processing systems. 2011: 109-117. [5] Chen L C, Papandreou G, Kokkinos I, et al. Semantic image segmentation with deep convolutional nets and fully connected crfs[J]. arXiv preprint arXiv:1412.7062, 2014. [6] Zheng S, Jayasumana S, Romera-Paredes B, et al. Conditional random fields as recurrent neural networks[C]//Proceedings of the IEEE international conference on computer vision. 2015: 1529-1537. [7] Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions[J]. arXiv preprint arXiv:1511.07122, 2015. [8] Liu W, Rabinovich A, Berg A C. Parsenet: Looking wider to see better[J]. arXiv preprint arXiv:1506.04579, 2015. [9] Zhao H, Shi J, Qi X, et al. Pyramid scene parsing network[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 2881-2890. 2.3【每周CV论文推荐】 初学GAN必须要读的文章 [1] Ng A Y, Jordan M I. On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes[C]//Advances in neural information processing systems. 2002: 841-848. [2] Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]//Advances in neural information processing systems. 2014: 2672-2680. [3] Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. arXiv preprint arXiv:1511.06434, 2015. [4] Mirza M, Osindero S. Conditional generative adversarial nets[J]. arXiv preprint arXiv:1411.1784, 2014. [5] Chen X, Duan Y, Houthooft R, et al. Infogan: Interpretable representation learning by information maximizing generative adversarial nets[C]//Advances in neural information processing systems. 2016: 2172-2180. [6] Denton E L, Chintala S, Fergus R. Deep generative image models using a laplacian pyramid of adversarial networks[C]//Advances in neural information processing systems. 2015: 1486-1494. [7] Huang X, Li Y, Poursaeed O, et al. Stacked generative adversarial networks[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR). [8] Karras T, Aila T, Laine S, et al. Progressive growing of gans for improved quality, stability, and variation[J]. arXiv preprint arXiv:1710.10196, 2017. 2.4【每周CV论文】初学GAN图像风格化必须要读的文章 [1] Isola P, Zhu J Y, Zhou T, et al. Image-to-image translation with conditional adversarial networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 1125-1134. [2] Zhu J Y, Park T, Isola P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]//Proceedings of the IEEE international conference on computer vision. 2017: 2223-2232. [3] Liu M, Breuel T M, Kautz J, et al. Unsupervised Image-to-Image Translation Networks[C]. neural information processing systems, 2017: 700-708. [4] Choi Y, Choi M, Kim M, et al. Stargan: Unified generative adversarial networks for multi-domain image-to-image translation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 8789-8797. [5] Choi Y, Uh Y, Yoo J, et al. StarGAN v2: Diverse Image Synthesis for Multiple Domains[J]. arXiv: Computer Vision and Pattern Recognition, 2019. [6] Karras T, Laine S, Aila T. A style-based generator architecture for generative adversarial networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 4401-4410. [7] Karras T, Laine S, Aittala M, et al. Analyzing and Improving the Image Quality of StyleGAN.[J]. arXiv: Computer Vision and Pattern Recognition, 2019. [8] Li T, Qian R, Dong C, et al. BeautyGAN: Instance-level Facial Makeup Transfer with Deep Generative Adversarial Network[C]. acm multimedia, 2018: 645-653. [9] Kim J, Kim M, Kang H, et al. U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation[C]. international conference on learning representations, 2020. 3. 人脸领域 计算机视觉里除了人脸图像，没有一个领域可以覆盖底层图像特征，目标检测与跟踪，图像分类和检索，图像滤波，图像分割，三维重建，风格迁移等方向，并且能够做到相互融合从而进行工业界落地，因此我们肯定要学习人脸图像。 3.1【每周CV论文推荐】 深度学习人脸检测入门必读文章 [1] Yang S, Luo P, Loy C C, et al. Faceness-net: Face detection through deep facial part responses[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 40(8): 1845-1859. [2] Li H, Lin Z, Shen X, et al. A convolutional neural network cascade for face detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 5325-5334. [3] Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks[K. Zhang al., 2016 [4] Jiang H, Learned-Miller E. Face detection with the faster R-CNN[C]//2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017). IEEE, 2017: 650-657. [5] Wang H, Li Z, Ji X, et al. Face R-CNN[J]. 2017. [6] Huang L, Yang Y, Deng Y, et al. Densebox: Unifying landmark localization with end to end object detection[J]. arXiv preprint arXiv:1509.04874, 2015. 3.2【每周CV论文推荐】 初学深度学习人脸关键点检测必读文章 [1] Sun Y, Wang X, Tang X. Deep convolutional network cascade for facial point detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2013: 3476-3483. [2] Zhang Z, Luo P, Loy C C, et al. Facial landmark detection by deep multi-task learning[C]//European conference on computer vision. Springer, Cham, 2014: 94-108. [3] Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks[K. Zhang al., 2016 [4] Kowalski M, Naruniec J, Trzcinski T. Deep alignment network: A convolutional neural network for robust face alignment[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2017: 88-97. [5] Wu W, Qian C, Yang S, et al. Look at boundary: A boundary-aware face alignment algorithm[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 2129-2138. [6] Jin X, Tan X. Face alignment in-the-wild: A survey[J]. Computer Vision and Image Understanding, 2017, 162: 1-22. 3.3 【每周CV论文推荐】 初学深度学习人脸识别和验证必读文章 [1] Taigman Y, Yang M, Ranzato M A, et al. Deepface: Closing the gap to human-level performance in face verification[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2014: 1701-1708. [2] Sun Y, Wang X, Tang X. Deep learning face representation from predicting 10,000 classes[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2014: 1891-1898. [3] Sun Y, Chen Y, Wang X, et al. Deep learning face representation by joint identification-verification[C]//Advances in neural information processing systems. 2014: 1988-1996. [4] Sun Y, Wang X, Tang X. Deeply learned face representations are sparse, selective, and robust[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 2892-2900. [5] Schroff F, Kalenichenko D, Philbin J. Facenet: A unified embedding for face recognition and clustering[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 815-823. [6] Parkhi, Omkar M., Andrea Vedaldi, and Andrew Zisserman. "Deep face recognition." bmvc. Vol. 1. No. 3. 2015. [7] Wen Y, Zhang K, Li Z, et al. A discriminative feature learning approach for deep face recognition[C]//European conference on computer vision. Springer, Cham, 2016: 499-515. [8] Zhang X, Fang Z, Wen Y, et al. Range loss for deep face recognition with long-tailed training data[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 5409-5418. [9] Wang J, Zhou F, Wen S, et al. Deep metric learning with angular loss[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 2593-2601. [10] Liu Y, Li H, Wang X. Rethinking feature discrimination and polymerization for large-scale recognition[J]. arXiv preprint arXiv:1710.00870, 2017. 3.4【每周CV论文推荐】 初学深度学习人脸属性分析必读的文章 [1] Rothe R, Timofte R, Van Gool L. Deep expectation of real and apparent age from a single image without facial landmarks[J]. International Journal of Computer Vision, 2018, 126(2-4): 144-157. [2] Fabian Benitez-Quiroz C, Srinivasan R, Martinez A M. Emotionet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 5562-5570. [3] Liang L, Lin L, Jin L, et al. SCUT-FBP5500: A diverse benchmark dataset for multi-paradigm facial beauty prediction[C]//2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018: 1598-1603. [4] Liu Z, Luo P, Wang X, et al. Deep learning face attributes in the wild[C]//Proceedings of the IEEE international conference on computer vision. 2015: 3730-3738. [5] Lee C H, Liu Z, Wu L, et al. MaskGAN: towards diverse and interactive facial image manipulation[J]. arXiv preprint arXiv:1907.11922, 2019. [6] Zheng X, Guo Y, Huang H, et al. A Survey to Deep Facial Attribute Analysis[J]. arXiv preprint arXiv:1812.10265, 2018. 3.5 【每周CV论文推荐】 初学活体检测与伪造人脸检测必读的文章 [1] Yang J, Lei Z, Li S Z. Learn convolutional neural network for face anti-spoofing[J]. arXiv preprint arXiv:1408.5601, 2014. [2] Atoum Y, Liu Y, Jourabloo A, et al. Face anti-spoofing using patch and depth-based CNNs[C]//2017 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2017: 319-328. [3] Liu Y, Jourabloo A, Liu X. Learning deep models for face anti-spoofing: Binary or auxiliary supervision[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 389-398. [4] Jourabloo A, Liu Y, Liu X. Face de-spoofing: Anti-spoofing via noise modeling[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 290-306. [5] Zhang S, Wang X, Liu A, et al. A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 919-928. [6] Rössler A, Cozzolino D, Verdoliva L, et al. Faceforensics++: Learning to detect manipulated facial images[J]. arXiv preprint arXiv:1901.08971, 2019. [7] Wang R, Ma L, Juefei-Xu F, et al. FakeSpotter: A Simple Baseline for Spotting AI-Synthesized Fake Faces[J]. arXiv preprint arXiv:1909.06122, 2019. 3.6【每周CV论文推荐】 初学深度学习单张图像三维人脸重建需要读的文章 [1] Blanz V, Vetter T. A morphable model for the synthesis of 3D faces[C]//Siggraph. 1999, 99(1999): 187-194. [2] Cao C, Weng Y, Zhou S, et al. Facewarehouse: A 3d facial expression database for visual computing[J]. IEEE Transactions on Visualization and Computer Graphics, 2013, 20(3): 413-425. [3] Tuan Tran A, Hassner T, Masi I, et al. Regressing robust and discriminative 3D morphable models with a very deep neural network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 5163-5172. [4] Chang F J, Tuan Tran A, Hassner T, et al. Faceposenet: Making a case for landmark-free face alignment[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 1599-1608. [5] Chang F J, Tran A T, Hassner T, et al. ExpNet: Landmark-free, deep, 3D facial expressions[C]//2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018). IEEE, 2018: 122-129. [6] Jackson A S, Bulat A, Argyriou V, et al. Large pose 3D face reconstruction from a single image via direct volumetric CNN regression[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 1031-1039. [7] Feng Y, Wu F, Shao X, et al. Joint 3d face reconstruction and dense alignment with position map regression network[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 534-551. [8] Tewari A, Zollhofer M, Kim H, et al. Mofa: Model-based deep convolutional face autoencoder for unsupervised monocular reconstruction[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 1274-1283. [9] Zheng X, Guo Y, Huang H, et al. A Survey to Deep Facial Attribute Analysis[J]. arXiv preprint arXiv:1812.10265, 2018. 3.7【每周CV论文推荐】 人脸识别剩下的难题：从遮挡，年龄，姿态，妆造到亲属关系，人脸攻击 [1] Wang J, Yuan Y, Yu G. Face attention network: An effective face detector for the occluded faces[J]. arXiv preprint arXiv:1711.07246, 2017. [2] Yuan X, Park I K. Face De-occlusion using 3D Morphable Model and Generative Adversarial Network[J]. arXiv preprint arXiv:1904.06109, 2019. [3] Sawant M M, Bhurchandi K M. Age invariant face recognition: a survey on facial aging databases, techniques and effect of aging[J]. Artificial Intelligence Review, 2019, 52(2): 981-1008. [4] Ding C, Tao D. A comprehensive survey on pose-invariant face recognition[J]. ACM Transactions on intelligent systems and technology (TIST), 2016, 7(3): 37. [5] Li Y, Song L, Wu X, et al. Anti-Makeup: Learning a bi-level adversarial network for makeup-invariant face verification[C]//Thirty-Second AAAI Conference on Artificial Intelligence. 2018. [6] Robinson J P, Shao M, Wu Y, et al. Families in the wild (fiw): Large-scale kinship image database and benchmarks[C]//Proceedings of the 24th ACM international conference on Multimedia. ACM, 2016: 242-246. [7] Rössler A, Cozzolino D, Verdoliva L, et al. Faceforensics: A large-scale video dataset for forgery detection in human faces[J]. arXiv preprint arXiv:1803.09179, 2018. 3.8【每周CV论文推荐】换脸算法都有哪些经典的思路？ [1] Suwajanakorn S, Seitz S M, Kemelmacher-Shlizerman I. What makes tom hanks look like tom hanks[C]//Proceedings of the IEEE International Conference on Computer Vision. 2015: 3952-3960. [2] Thies J, Zollhofer M, Stamminger M, et al. Face2face: Real-time face capture and reenactment of rgb videos[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 2387-2395. [3] https://github.com/hrastnik/FaceSwap [4] Korshunova I, Shi W, Dambre J, et al. Fast face-swap using convolutional neural networks[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 3677-3685. [5] Jin X, Qi Y, Wu S. Cyclegan face-off[J]. arXiv preprint arXiv:1712.03451, 2017. [6] Korshunov P, Marcel S. Deepfakes: a new threat to face recognition? assessment and detection[J]. arXiv preprint arXiv:1812.08685, 2018. [7] https://github.com/deepfakes/faceswap 4.图像质量领域 图像质量领域是图像处理算法的起源，包含了很多方向，也是我们需要重点学习的内容。 4.1 【每周CV论文】深度学习图像降噪应该从阅读哪些文章开始 [1] Mao X, Shen C, Yang Y B. Image restoration using very deep convolutionalencoder-decoder networks with symmetric skip connections[C]//Advances in neuralinformation processing systems. 2016: 2802-2810. [2] Zhang K, Zuo W, Chen Y, et al. Beyond a gaussian denoiser: Residual learningof deep cnn for image denoising[J]. IEEE Transactions on Image Processing, 2017,26(7): 3142-3155. [3] Guo S, Yan Z, Zhang K, et al. Toward convolutional blind denoising of realphotographs[C]//Proceedings of the IEEE Conference on Computer Vision andPattern Recognition. 2019: 1712-1722. [4] Chen J, Chen J, Chao H, et al. Image blind denoising with generative adversarial network based noise modeling[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 3155-3164. [5] Lehtinen J, Munkberg J, Hasselgren J, et al. Noise2noise: Learning image restoration without clean data[J]. arXiv preprint arXiv:1803.04189, 2018. [6] Lempitsky V, Vedaldi A, Ulyanov D, et al. Deep Image Prior[C]. computer vision and pattern recognition, 2018: 9446-9454. [7] Krull A, Buchholz T, Jug F, et al. Noise2Void - Learning Denoising From Single Noisy Images[C]. computer vision and pattern recognition, 2019: 2129-2137. [8] Batson J, Royer L. Noise2Self: Blind Denoising by Self-Supervision[J]. arXiv: Computer Vision and Pattern Recognition, 2019. [9] Li S, Cao X, Araujo I B, et al. Single Image Deraining: A Comprehensive Benchmark Analysis[C]. computer vision and pattern recognition, 2019: 3838-3847. 4.2【每周CV论文】初学深度学习图像对比度增强应该要读的文章 [1] Chen Q, Xu J, Koltun V. Fast image processing with fully-convolutionalnetworks[C]//Proceedings of the IEEE International Conference on ComputerVision. 2017: 2497-2506. [2] Talebi H, Milanfar P. Learned perceptual image enhancement[C]//2018 IEEEInternational Conference on Computational Photography (ICCP). IEEE, 2018: 1-13. [3] Wei C, Wang W, Yang W, et al. Deep Retinex Decomposition for Low-Light Enhancement.[C]. british machine vision conference, 2018. [4] Ignatov A, Kobyshev N, Timofte R, et al. DSLR-quality photos on mobiledevices with deep convolutional networks[C]//Proceedings of the IEEEInternational Conference on DSLR-quality photos on mobile Computer Vision.2017: 3277-3285. [5] Gharbi M, Chen J, Barron J T, et al. Deep bilateral learning for real-timeimage enhancement[J]. ACM Transactions on Graphics (TOG), 2017, 36(4): 118. [6] Hu Y, He H, Xu C, et al. Exposure: A white-box photo post-processing framework[J]. ACM Transactions on Graphics (TOG), 2018, 37(2): 26. [7] Chen C, Chen Q, Xu J, et al. Learning to see in the dark[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 3291- 3300. 4.3【每周CV论文】初学深度学习图像超分辨应该要读的文章 [1] Dong C, Loy C C, He K, et al. Image super-resolution using deep convolutional networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 38(2): 295-307. [2] Shi W, Caballero J, Huszar F, et al. Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network[C]. computer vision and pattern recognition, 2016: 1874-1883. [3] Johnson J, Alahi A, Fei-Fei L. Perceptual losses for real-time style transfer and super-resolution[C]//European conference on computer vision. Springer, Cham, 2016: 694-711. [4] Ledig C, Theis L, Huszár F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4681-4690. [5] Bulat A, Yang J, Tzimiropoulos G. To learn image super-resolution, use a gan to learn how to do image degradation first[C]//Proceedings of the European conference on computer vision (ECCV). 2018: 185-200. [6] Chen Y, Tai Y, Liu X, et al. FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors[C]. computer vision and pattern recognition, 2018: 2492-2501. 4.4【每周CV论文】初学深度学习图像修复应该要读的文章 [1] Pathak D, Krahenbuhl P, Donahue J, et al. Context encoders: Feature learning by inpainting[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 2536-2544. [2] Iizuka S, Simo-Serra E, Ishikawa H. Globally and locally consistent image completion[J]. ACM Transactions on Graphics (ToG), 2017, 36(4): 1-14. [3] Yu J, Lin Z, Yang J, et al. Generative image inpainting with contextual attention[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 5505-5514. [4] Liu G, Reda F A, Shih K J, et al. Image inpainting for irregular holes using partial convolutions[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 85-100. [5] Nazeri K, Ng E, Joseph T, et al. EdgeConnect: Structure Guided ImageInpainting using Edge Prediction[C]//Proceedings of the IEEE InternationalConference on Computer Vision Workshops. 2019: 0-0. [6] Wan Z , Zhang B , Chen D , et al. Bringing Old Photos Back to Life[J]. 2020.. 5.如何交流学习这些内容 5.1 论文下载 开始的时候我们将论文放在了git项目中，但是随着数量增加不合适再放到git中，大家可以自己下载，也可以在我们的知识星球中下载。  5.2 文章解读 各个方向的文章解读也在有三AI知识星球中，目前已经有超过300期。   5.3 有三的书 另外一些方向也已经出版成书，比如人脸方向，模型方向，可以大家去我的三本书中阅读。   转载文章请后台联系 侵权必究   |
|
|
