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2 | Presenter | Title | Paper/Project/Video/Slides links | short description | Slides | |||||||||||||||||||||
3 | instructors | Introduction and scheduling | ||||||||||||||||||||||||
4 | Hieu | GAN: a tutorial | ||||||||||||||||||||||||
5 | 2/5/2020 | |||||||||||||||||||||||||
6 | Presenter | Title (Generative models) | Paper/Project/Video/Slides links | short description | Slides | |||||||||||||||||||||
7 | Cristina, Vinh | Auto-Encoding Variational Bayes Diederik P. Kingma Max Welling Ma | https://arxiv.org/pdf/1312.6114.pdf | https://docs.google.com/presentation/d/1JwNBd8H_iwE1O-GPXF-eTN4kz6yWgWznC1av3NDHeJQ/edit?usp=sharing | ||||||||||||||||||||||
8 | Autoencoding beyond pixels using a learned similarity metric Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, Ole Winther ICML 2016 | https://arxiv.org/pdf/1512.09300.pdf | ||||||||||||||||||||||||
9 | Boyu, Tao | Generating Diverse High-Fidelity Images with VQ-VAE-2 Ali Razavi, Aaron van den Oord, Oriol Vinyals arxiv 2019 | https://arxiv.org/pdf/1906.00446.pdf | https://docs.google.com/presentation/d/1NCQ5lm8mtprYN12AkEyUXDEHQkPwVrLL/edit | ||||||||||||||||||||||
10 | Viresh, Pratyush | CycleGAN: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks Jun-Yan Zhu∗ Taesung Park∗ Phillip Isola Alexei A. Efros CVPR 2017 | https://arxiv.org/pdf/1703.10593.pdf | https://drive.google.com/file/d/1zNYFgy9gWnM1lvOBXukGgkS65yJ82AqN/view?usp=sharing | ||||||||||||||||||||||
11 | Jingyi, Hieu | StyleGAN:A Style-Based Generator Architecture for Generative Adversarial Networks Tero Karras, Samuli Laine, Timo Aila CVPR 2019 | https://arxiv.org/pdf/1812.04948.pdf | https://1drv.ms/p/s!AjfgrkEf5X5RgRtA93YkTeOBC_ju | ||||||||||||||||||||||
12 | Huidong, Pavani | SinGAN: Learning a Generative Model from a Single Natural Image Tamar Rott Shaham Tali Dekel Tomer Michaeli ICCV 2019 | http://openaccess.thecvf.com/content_ICCV_2019/papers/Shaham_SinGAN_Learning_a_Generative_Model_From_a_Single_Natural_Image_ICCV_2019_paper.pdf | |||||||||||||||||||||||
13 | 2/12/2020 | |||||||||||||||||||||||||
14 | Presenter | Title (Tracking) | Paper/Project/Video/Slides links | short description | Slides | |||||||||||||||||||||
15 | Haibin Ling | Introduction to tracking | ||||||||||||||||||||||||
16 | Heng Fan, Supreeth | Goutam Bhat, Martin Danelljan, Luc Van Gool, Radu Timofte. Learning Discriminative Model Prediction for Tracking. ICCV 2019 | http://openaccess.thecvf.com/content_ICCV_2019/papers/Bhat_Learning_Discriminative_Model_Prediction_for_Tracking_ICCV_2019_paper.pdf | https://docs.google.com/presentation/d/1pWe5xZ-ucxYxIPaedTo4lstzhfI52Iu6R2zv96gl6lo/edit#slide=id.p | ||||||||||||||||||||||
17 | Jiaxiang, Lei Zhou | Ziyuan Huang, Changhong Fu, Yiming Li, Fuling Lin, Peng Lu. Learning Aberrance Repressed Correlation Filters for Real-Time UAV Tracking. ICCV 2019 | https://arxiv.org/pdf/1908.02231.pdf | https://docs.google.com/presentation/d/1eGi2J27muWixzjpmChcILMjBA5ln-2W4bGX9W99v0cA/edit?usp=sharing | ||||||||||||||||||||||
18 | Georgi, Cristina | Yaadhav Raaj, Haroon Idrees, Gines Hidalgo, Yaser Sheikh. Efficient Online Multi-Person 2D Pose Tracking With Recurrent Spatio-Temporal Affinity Fields. CVPR 2019 | https://arxiv.org/pdf/1811.11975.pdf | https://docs.google.com/presentation/d/19nLsKMVxYXYPCWAw7l5wHeUVTYat_8ilCQVGGHHudko/edit?usp=sharing | ||||||||||||||||||||||
19 | Hieu, Ruyi | https://docs.google.com/spreadsheets/d/1Vbxrufq672N-fK6G-ppuu60nhGq57Fr1llhKfpWTAIM/edit?usp=sharing | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wiyatno_Physical_Adversarial_Textures_That_Fool_Visual_Object_Tracking_ICCV_2019_paper.pdf | first adversarial attack for sequential tracking systems | https://docs.google.com/presentation/d/1T8hH7iqhE9Zgew4cSqSIIedBT4MBLg5DV6swjpaN3Iw/edit?usp=sharing | |||||||||||||||||||||
20 | Vu, David P | Yaron Meirovitch, Lu Mi, Hayk Saribekyan, Alexander Matveev, David Rolnick, Nir Shavit. Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics. CVPR 2019. | https://arxiv.org/pdf/1812.01157.pdf | https://docs.google.com/presentation/d/1XLgiU-VnR_h0MiV18A-IRkcBRxYtbkjCzDm1WhjFYrs/edit?usp=sharing | ||||||||||||||||||||||
21 | 2/19/2020 | |||||||||||||||||||||||||
22 | Presenter | Title (Video Representation) | Paper/Project/Video/Slides links | short description | Slides | |||||||||||||||||||||
23 | Zhibo, Souradeep | Carreira and Zisserman, Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset, 2017 | https://arxiv.org/pdf/1705.07750.pdf | https://docs.google.com/presentation/d/1yLgIAnxbqnKd74j6GMIyV9otLj8sIz_sN8NQI9mSQao/edit?usp=sharing | ||||||||||||||||||||||
24 | Bingyao, Vinh | Yeung et al., End-to-end Learning of Action Detection from Frame Glimpses in Videos, 2016 | https://arxiv.org/pdf/1511.06984.pdf | https://docs.google.com/presentation/d/1GbyQ5HWPzNDoFNwZRh_rGymwo_Iy1rnQ28oCzadatjI/edit?usp=sharing | ||||||||||||||||||||||
25 | Qiaomu, Shahira | Wang et al., Non-local Neural Networks, 2017 | https://arxiv.org/pdf/1711.07971v3.pdf | https://docs.google.com/presentation/d/1bHnIz2L8Az29J5tgOw3fKos3ejrm8308otz2FXYAZaI/edit?pli=1#slide=id.g52ca06d2f6_0_182 | ||||||||||||||||||||||
26 | David B, Kumara | Wang and Gupta, Videos as Space-Time Region Graphs, 2018 | https://eccv2018.org/openaccess/content_ECCV_2018/papers/Xiaolong_Wang_Videos_as_Space-Time_ECCV_2018_paper.pdf | https://docs.google.com/presentation/d/18ZplAT-k2K_HqJdeyWzVrxz77V7ZYA_BHUu-abHm6fs/edit?usp=sharing | ||||||||||||||||||||||
27 | Bo (Bryan), Zekun | Piergiovanni and Ryoo, Temporal Gaussian Mixture Layer for Videos, 2019 | https://arxiv.org/pdf/1803.06316.pdf | A fully differentiable layer with few parameters to learn temporal structure in videos. | https://docs.google.com/presentation/d/1qk4ov4oil78MQfD7g8lCsg_jgDd5jSvZ-y1HOXlELWQ/edit?usp=sharing | |||||||||||||||||||||
28 | 2/26/2020 | |||||||||||||||||||||||||
29 | Presenter | Paper/Project/Video/Slides links | short description | Slides | ||||||||||||||||||||||
30 | Viresh, Bo (Bryan) | Crowdcounting: Colab setup | ||||||||||||||||||||||||
31 | Leveraging Unlabeled Data for Crowd Counting by Learning to Rank, CVPR 2018 | https://arxiv.org/pdf/1803.03095.pdf | ||||||||||||||||||||||||
32 | Bayesian Loss for Crowd Count Estimation with Point Supervision, ICCV 2019 | https://arxiv.org/pdf/1908.03684.pdf | ||||||||||||||||||||||||
33 | 3/11/2020 | |||||||||||||||||||||||||
34 | Presenter | Title (Self-supervised learning) | Paper/Project/Video/Slides links | short description | Slides | |||||||||||||||||||||
35 | Cristina, Qiaomu | Doersch et al., Unsupervised Visual Representation Learning by Context Prediction, 2015 | https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Doersch_Unsupervised_Visual_Representation_ICCV_2015_paper.pdf | https://docs.google.com/presentation/d/18NTBaOoOQHZdGVI6VRfKOUs5NIivXsKy0RgdAgBD980/edit?ts=5e6678c4#slide=id.g81461fa5ad_0_0 | ||||||||||||||||||||||
36 | Ruyi, Kumara | Pathak et al., Context Encoders: Feature Learning by Inpainting, 2016 | https://arxiv.org/pdf/1604.07379.pdf | an unsupervised visual feature learning algorithm based on filling in large missing areas of the image | https://docs.google.com/presentation/d/1vCAIEfK4yV5DK9_nMi8Fc-wpAc05XYbkBjzCjehZ0kI/edit?usp=sharing | |||||||||||||||||||||
37 | Supreeth, David P | Misra et al., Shuffle and Learn: Unsupervised Learning using Temporal Order Verification, 2016 | ` | https://docs.google.com/presentation/d/1rvghp_IRK_afCUJwEimxXHDZKW-mOJdivGD7XUtdm4A/edit#slide=id.g7f036733b3_0_11 | ||||||||||||||||||||||
38 | Prantik, David B | Wei et al., Learning and Using the Arrow of Time, 2018 | https://www.robots.ox.ac.uk/~vgg/publications/2018/Wei18/wei18.pdf | https://docs.google.com/presentation/d/18SWzTCt3WSGtvzV1YRVWOLx5RObZ4vUeGH0aPdSK0CE/edit?usp=drivesdk | ||||||||||||||||||||||
39 | Bo, Xiaoling | Jang et al., Grasp2Vec: Learning Object Representations from Self-Supervised Grasping, 2018 | http://proceedings.mlr.press/v87/jang18a/jang18a.pdf | A self-supervised approach to generating labels for representation learning and object-centric instance grasping. | https://docs.google.com/presentation/d/1zSBj-5ZUUMnEhUflKxtwlMd1s_BY1XGpycdO4cSWtQc/edit?usp=sharing | |||||||||||||||||||||
40 | (New optional reading) | Piergiovanni et al., Evolving Losses for Unsupervised Video Representation Learning, 2020 | https://arxiv.org/pdf/2002.12177.pdf | Please check if you are interested in knowing more about self-supervised learning for videos | ||||||||||||||||||||||
41 | 4/1/2020 | |||||||||||||||||||||||||
42 | Presenter | Title (Scene text recognition) | Paper/Project/Video/Slides links | short description | Slides | |||||||||||||||||||||
43 | Minh | Convolutional Character Networks. Linjie Xing, Zhi Tian, Weilin Huang and Matthew Scott. ICCV'19 | http://openaccess.thecvf.com/content_ICCV_2019/papers/Xing_Convolutional_Character_Networks_ICCV_2019_paper.pdf | Every student needs to read this paper and submit the paper summary | https://www.dropbox.com/s/4s4ou1dueqvzgs9/02_SceneTextRecognition.pdf?dl=0 | |||||||||||||||||||||
44 | Minh | ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network. Y. Liu, H. Chen, C. Shen, T. He, L. Jin, L. Wang. CVPR'20 | https://arxiv.org/pdf/2002.10200 | Every student needs to read this paper and submit the paper summary | https://www.dropbox.com/s/4s4ou1dueqvzgs9/02_SceneTextRecognition.pdf?dl=0 | |||||||||||||||||||||
45 | Minh | Real-time Scene Text Detection with Differentiable Binarization. Minghui Liao, Zhaoyi Wan, Cong Yao, Kai Chen, Xiang Bai. AAAI'20 | https://aaai.org/Papers/AAAI/2020GB/AAAI-LiaoM.578.pdf | All students do not need to read this paper, unless interested. | https://www.dropbox.com/s/4s4ou1dueqvzgs9/02_SceneTextRecognition.pdf?dl=0 | |||||||||||||||||||||
46 | Zekun, Souradeep | Scene Text Recognition via Transformer. Xinjie Feng, Hongxun Yao, Yuankai Yi, Jun Zhang, and Shengping Zhang. ArXiv 2020. | https://arxiv.org/abs/2003.08077 | Except for Zekun and Souradeep, other students don't need to read this paper. | ||||||||||||||||||||||
47 | Sagnik | Colab 1 | https://github.com/MhLiao/DB | Colab | https://docs.google.com/document/d/19Qm9Kh0nVBbKjgTt4T2n-THiYVAot-y3q7z2Te6sZ9E/edit?usp=sharing | |||||||||||||||||||||
48 | Mingzhen | Colab 2 | https://github.com/MalongTech/research-charnet | Colab | https://www.dropbox.com/s/mxvoku6rh8bzq06/CharNet.zip?dl=0 | |||||||||||||||||||||
49 | Georgi, Prantik | Y. Xu, Y. Wang, W. Zhou, Y. Wang, Z. Yang, and X. Bai. Textfield: Learning a deep direction field for irregular scene text detection. IEEE TIP 2019 | https://arxiv.org/abs/1812.01393 | Except for Georgi and Prantik, other students don't need to read this paper. | https://docs.google.com/presentation/d/1ksaTgk2PGPX9fNQLmOuGyD3EfhmKw_3El-ymQQZ5vBM/edit?usp=sharing | |||||||||||||||||||||
50 | 4/8/2020 | |||||||||||||||||||||||||
51 | Presenter | Title (Visual SLAM) | Paper/Project/Video/Slides links | short description | Slides | |||||||||||||||||||||
52 | Georgi, Prantik | Y. Xu, Y. Wang, W. Zhou, Y. Wang, Z. Yang, and X. Bai. Textfield: Learning a deep direction field for irregular scene text detection. IEEE TIP 2019 | https://arxiv.org/abs/1812.01393 | Except for Georgi and Prantik, other students don't need to read this paper. | https://docs.google.com/presentation/d/1ksaTgk2PGPX9fNQLmOuGyD3EfhmKw_3El-ymQQZ5vBM/edit?usp=sharing | |||||||||||||||||||||
53 | Bo, Pratyush | Raul Mur-Artal, J. M. M. Montiel, Juan D. Tardos ORB-SLAM: a Versatile and Accurate Monocular SLAM SystemIEEE T-Robotics, 2015 | https://arxiv.org/pdf/1502.00956.pdf | A complete system using the same ORB features for all SLAM tasks: tracking, mapping, relocalization, and loop closing. | https://docs.google.com/presentation/d/1GvqCLGvobM0bqFW6gnIzAxIrE2VyFplfgEkgNv34sao/edit?usp=sharing | |||||||||||||||||||||
54 | Heng, Shahira | Richard A. Newcombe ; Steven J. Lovegrove ; Andrew J. Davison DTAM: Dense tracking and mapping in real-time ICCV 2011 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6126513 | https://docs.google.com/presentation/d/1w9i1Gl0PvJKVJhiamgLdI07kDGbQJksOCHhYLRtfoy8/edit?ts=5e8dfd9f#slide=id.g830fadcdf6_1_19 | ||||||||||||||||||||||
55 | Georgi, Pavani | Michael Bloesch, Tristan Laidlow, Ronald Clark, Stefan Leutenegger, Andrew J. Davison. Learning Meshes for Dense Visual SLAM. ICCV 2019 | http://openaccess.thecvf.com/content_ICCV_2019/papers/Bloesch_Learning_Meshes_for_Dense_Visual_SLAM_ICCV_2019_paper.pdf | https://docs.google.com/presentation/d/1cPGD03BdLTg-woRjDiRHm3-iXb10ojlBEcuzwGqMWCE/edit?usp=sharing | ||||||||||||||||||||||
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58 | 4/15/2020 | |||||||||||||||||||||||||
59 | Presenter | Title (Optimal Transport) | Paper/Project/Video/Slides links | short description | Slides | |||||||||||||||||||||
60 | Huidong | A brief introduction to optimal transport | https://docs.google.com/presentation/d/1jkrKsXFduCctNSJ6MpKWCFQ6BwEVwrPJYTFBVGijdLM/edit#slide=id.p | |||||||||||||||||||||||
61 | Kumara, Lei | Wasserstein Generative Adversarial Networks, ICML 2017 | https://arxiv.org/abs/1701.07875 | WGAN | https://docs.google.com/presentation/d/1jkrKsXFduCctNSJ6MpKWCFQ6BwEVwrPJYTFBVGijdLM/edit#slide=id.p | |||||||||||||||||||||
62 | Tao, Zhibo | Improved training of Wasserstein GANs, NeurIPS 2018 | https://arxiv.org/pdf/1704.00028.pdf | WGAN-GP | https://docs.google.com/presentation/d/1jkrKsXFduCctNSJ6MpKWCFQ6BwEVwrPJYTFBVGijdLM/edit#slide=id.p | |||||||||||||||||||||
63 | David B, Mingzhen | Spectral Normalization for Generative Adversarial Networks, ICLR 2018 | https://arxiv.org/abs/1802.05957 | Spectral Normalization | https://docs.google.com/presentation/d/1jkrKsXFduCctNSJ6MpKWCFQ6BwEVwrPJYTFBVGijdLM/edit#slide=id.p | |||||||||||||||||||||
64 | 4/22/2020 | |||||||||||||||||||||||||
65 | Presenter | Title (Flow Models) | Paper/Project/Video/Slides links | short description | Slides | |||||||||||||||||||||
66 | Jingyi, Vu | Diederik P. Kingma, and Prafulla Dhariwal. “Glow: Generative flow with invertible 1x1 convolutions.” NIPS 2018. | https://arxiv.org/pdf/1807.03039.pdf | https://drive.google.com/file/d/1EenQ7OqmYl2cdjR0aUQaUaj1PM2fMEj8/view?usp=sharing | ||||||||||||||||||||||
67 | Lei, Sagnik | C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds | https://arxiv.org/pdf/1912.07009.pdf | https://docs.google.com/presentation/d/1qtz777CROaMTy4MqOw_W2llu0iUm9MrHXIEUgGluJiM/edit?usp=sharing | ||||||||||||||||||||||
68 | Huidong, Tao | Implicit Generation and Generalization Methods for Energy-Based Models | https://arxiv.org/pdf/1903.08689.pdf | IGEBM: Good quality results on ImageNet | https://docs.google.com/presentation/d/1X3Z9S1TUhwwtmJuAvvlfmr-_xAh3Qy-UecHH669e5SA/edit?ts=5e9db9b2#slide=id.p | |||||||||||||||||||||
69 | Prantik, Xiaoling | Your classifier is secretly an energy based model and you should treat it like one, ICLR2020 | https://openreview.net/pdf?id=Hkxzx0NtDB | Joint Energy Based Model: Creates a generative model using a standard image classifier. It unifies generative and discriminative models quite nicely and is an insightful read. | https://docs.google.com/presentation/d/1sgHldKbpNPAeCK1cjz7sVRHyZSPIfvOHIp54nRHc18w/edit?usp=sharing | |||||||||||||||||||||
70 | Souradeep, Ruyi | Do Deep Generative Models Know What They Don't Know? | https://arxiv.org/pdf/1810.09136.pdf | This is a good paper on the shortcomings of current generative models and can provide interesting research directions to look into. | https://docs.google.com/presentation/d/16XbERXrNXrDP5LRHVVRHTtlC5bE_EkDFIFv_2UvygFw/edit?usp=sharing | |||||||||||||||||||||
71 | 4/29/2020 | |||||||||||||||||||||||||
72 | Presenter | Title (Neural architecture search) | Paper/Project/Video/Slides links | short description | Slides | |||||||||||||||||||||
73 | Pratyush, Pavani | Zoph and Le, Neural Architecture Search with Reinforcement Learning, 2017 | https://arxiv.org/pdf/1611.01578.pdf | https://docs.google.com/presentation/d/1QZgbce781u9PP9FdFlf39TITBhPZ_Nid/edit#slide=id.p1 | ||||||||||||||||||||||
74 | Xiaoling, Shahira | Liu et al., DARTS: Differentiable Architecture Search, ICLR 2019 | https://arxiv.org/pdf/1806.09055.pdf | https://docs.google.com/presentation/d/1Wsz3Fs_kN_A3IZCFw0HItCT5wso60uK7T0kXElVO0J8/edit?usp=sharing | ||||||||||||||||||||||
75 | Vu, Vinh | Tan and Le, EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks, 2019 | https://arxiv.org/pdf/1905.11946.pdf | https://docs.google.com/presentation/d/1ZOhiFZO2Bt2Vy-iwEJQ4SNwJfvKdf88Ub1eQHM8rct0/edit?usp=sharing | ||||||||||||||||||||||
76 | Zekun, Supreeth | Xie et al., Exploring Randomly Wired Neural Networks for Image Recognition, 2019 | http://openaccess.thecvf.com/content_ICCV_2019/papers/Xie_Exploring_Randomly_Wired_Neural_Networks_for_Image_Recognition_ICCV_2019_paper.pdf | https://docs.google.com/presentation/d/17c7zK79i0LerJq2oI_ytq6Uk_vxqJDME9ClofkykNJ0/edit#slide=id.g7ff87733a4_5_29 | ||||||||||||||||||||||
77 | Michael Ryoo | Ryoo et al., AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures, 2019 | https://arxiv.org/pdf/1905.13209.pdf | |||||||||||||||||||||||
78 | 5/6/2020 | |||||||||||||||||||||||||
79 | Presenter | Title (Graph Neural Network) | Paper/Project/Video/Slides links | short description | Slides | |||||||||||||||||||||
80 | Qiaomu, Jingyi | Xinhong Ma, Tianzhu Zhang, Changsheng Xu. GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation. CVPR 2019. | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ma_GCAN_Graph_Convolutional_Adversarial_Network_for_Unsupervised_Domain_Adaptation_CVPR_2019_paper.pdf | https://docs.google.com/presentation/d/1OFdE_ZVCHrObqWgHgx23H1sWn9GIw1U30g8CZeP9t2Q/edit?usp=sharing | ||||||||||||||||||||||
81 | Jiaxiang, Sagnik | Jiwoong Park, Minsik Lee, Hyung Jin Chang, Kyuewang Lee, Jin Young Choi. Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning. ICCV 2019. | https://arxiv.org/pdf/1908.02441.pdf | https://docs.google.com/presentation/d/1BT-6HmiktHsQolxToaeem2IE3ajslluOKb1JplapMoQ/edit?usp=sharing | ||||||||||||||||||||||
82 | David P, Sagnik | Long Zhao, Xi Peng, Yu Tian, Mubbasir Kapadia, Dimitris N. Metaxas. Semantic Graph Convolutional Networks for 3D Human Pose Regression. CVPR 2019 | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_Semantic_Graph_Convolutional_Networks_for_3D_Human_Pose_Regression_CVPR_2019_paper.pdf | https://docs.google.com/presentation/d/1hv_1pQpAZg8kbsVyArxwGgq6s0CRe6q64MSbJKkMOlg/edit?usp=sharing | ||||||||||||||||||||||
83 | Heng, Jiaxiang | Paul Swoboda, Dagmar Kainm"uller, Ashkan Mokarian, Christian Theobalt, Florian Bernard. A Convex Relaxation for Multi-Graph Matching. CVPR 2019. | http://openaccess.thecvf.com/content_CVPR_2019/papers/Swoboda_A_Convex_Relaxation_for_Multi-Graph_Matching_CVPR_2019_paper.pdf | https://docs.google.com/presentation/d/1r54RnwfWdF6Tjh2IRt6tZdX8NSv-z3D3O53SUnYl5yE/edit#slide=id.g84b878a7d3_0_72 | ||||||||||||||||||||||
84 | Zhibo, Mingzhen | Jongmin Kim, Taesup Kim, Sungwoong Kim, Chang D. Yoo. Edge-Labeling Graph Neural Network for Few-Shot Learning. CVPR 2019. | https://arxiv.org/pdf/1905.01436.pdf | https://docs.google.com/presentation/d/1U4nXV0C66Muu6zjsmsbG1VKFpKMLuvq_h9zeR-0lJ2Q/edit?usp=sharing | ||||||||||||||||||||||
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