# cv-papers-with-code **Repository Path**: study_for_fun/cv-papers-with-code ## Basic Information - **Project Name**: cv-papers-with-code - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2018-11-15 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Computer Vision Papers with Code >I want to collect computer vision paper that has source code in github >Just for my fast-search & re-search. ^^ >If you find what I missed or If you want to add, please, let me know. # ECCV 2016 **An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem** Thorsten Beier, Bj ̈orn Andres†Ullrich K ̈othe, Fred A. Hamprech [[code1]](https://github.com/DerThorsten/nifty) [[code2]](https://github.com/DerThorsten/lifted_fusion_moves_eccv_2016) **Top-down Neural Attention by Excitation Backprop** Jianming Zhang, Zhe Lin, Jonathan Brandt, Xiaohui Shen, Stan Sclaroff [[code]](https://github.com/jimmie33/Caffe-ExcitationBP) **Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation** Muhammad Ghifary, W. Bastiaan Kleijn, Mengjie Zhang, David Balduzzi, Wen Li [[code]](https://github.com/ghif/drcn) **Identity Mappings in Deep Residual Networks** Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun [[code]](https://github.com/KaimingHe/resnet-1k-layers) **Deep Networks with Stochastic Depth** Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, Kilian Weinberger [[code]](https://github.com/yueatsprograms/Stochastic_Depth) **Heat Diffusion Long-Short Term Memory Learning for 3D Shape Analysis** Fan Zhu, Jin Xie and Yi Fang [[code]](https://github.com/blacksmithfan/HD_LSTM) **LIFT: Learned Invariant Feature Transform** Kwang Moo Yi, Eduard Trulls, Vincent Lepetit, and Pascal Fua [[code]](https://github.com/cvlab-epfl/LIFT) **Region-based semantic segmentationwith end-to-end training** [[code]](https://github.com/nightrome/matconvnet-calvin) **Projective Bundle Adjustment from Arbitrary Initialization Using the Variable Projection Method** Je Hyeong Hong, Christopher Zach, Andrew Fitzgibbon, Roberto Cipolla [[code]](https://github.com/jhh37/projective-ba) **Non-rigid 3D Shape Retrieval viaLarge Margin Nearest Neighbor Embedding** Ioannis Chiotellis, Rudolph Triebel, Thomas Windheuser, and Daniel Cremers [[code]](https://github.com/tum-vision/csd_lmnn) **Is Faster R-CNN Doing Well for Pedestrian Detection?** Liliang Zhang, Liang Lin, XiaodanLiang, KaimingHe [[code]](https://github.com/zhangliliang/RPN_BF) **Graph-Based Consistent Matching For Structure-From-Motion** Tianwei Shen, Siyu Zhu, Tian Fang, Runze Zhang, Long Quan [[code]](https://github.com/hlzz/libvot) **A Neural Approach to Blind Motion Deblurring** Ayan Chakrabarti [[code]](https://github.com/ayanc/ndeblur) **ATGV-Net: Accurate Depth Super-Resolution** Gernot Riegler, Matthias R ̈uther, Horst Bischof [[code]](https://github.com/griegler/primal-dual-networks) **Pixelwise View Selection for Unstructured Multi-View Stereo** Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm[[code]](https://github.com/colmap/colmap) **Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation** Golnaz Ghiasi, Charless C. Fowlkes [[code]](https://github.com/golnazghiasi/LRR) **Distractor-supported single target tracking in extremely cluttered scenes** Jingjing Xiao, Linbo Qiao, Rustam Stolkin, Ales Leonardis [[code]](https://github.com/shine636363/DSTcode) **View Synthesis by Appearance Flow** Tinghui Zhou, Shubham Tulsiani, Weilub Sun,Jitendra Mlik, Alexei A. Efros [[code]](https://github.com/tinghuiz/appearance-flow) **A Unified Multi-scale Deep Convolutional Neural Networkfor Fast Object Detection** Zhaowei Cai, Quanfu Fan, Rogerio Feris, Nuno Vasconcelos [[code]](https://github.com/zhaoweicai/mscnn) **Seed, Expand and Constrain: Three Principlesfor Weakly-Supervised Image Segmentation** Alexander Kolesnikov, Christoph H. Lampert [[code]](https://github.com/kolesman/SEC) **Deep3D : Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks** Junyuan Xie, Ross Girshik, Ali Farhadi [[code]](https://github.com/piiswrong/deep3d) **Fast, Exact & Multi-Scale Inference for Semantic Image Segmentation with Deep Gaussian CRFs** Siddhartha Chandra & Iasonas Kokkinos [[code]](https://github.com/siddharthachandra/gcrf) **A Generalized Successive Shortest Path Solver for Tracking Dividing Targets** Carsten Haubold, Janez Aleš, Steffen Wolf, Fred A. Hamprecht [[code1]](https://github.com/chaubold/dpct) [[code2]](https://github.com/chaubold/multiHypothesesTracking) **Accurate and Linear Time Pose Estimationfrom Points and Lines** Alexander Vakhitov, Jan Funke, Francesc Moreno-Noguer[[code]](https://github.com/alexander-vakhitov/pnpl) **Temporal Segment Networks:Towards Good Practices for Deep Action Recognition** Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang, Luc Van Gool [[code]](https://github.com/yjxiong/temporal-segment-networks) **Deep Markov Random Field for Image Modeling** Zhirong Wu, Dahua Lin, Xiaoou Tang [[code]](http://github.com/zhirongw/deep-mrf) **Depth--aawareVideoMagnification** Julian F. P. Kooij, Jan C. van Gemert [[code]]( github.com/jkooij/depthaware-moma) **Structured Matching for Phrase Localization** Mingzhe Wang, Mahmoud Azab, Noriyuki Kojima, Rada Mihalcea, Jia Deng [[code]](https://github.com/mingzhew/structured-matching) **Scalable Metric Learning via Weighted Approximate Rank Component Analysis** Cijo Jose, François, Fleuret [[code]](https://github.com/idiap/warca) **Resonant Deformable Matching:Simultaneous Registration and Reconstruction** John Corring, Anand Rangarajan [[code]](https://github.com/johncorring/RDM) **Deep Learning 3D Shape Surfaces using Geometry Images** Ayan Sinha, Jing Bai, Karthik Ramani [[code]](https://github.com/sinhayan/learning_geometry_images) **Do We Really Need to Collect Millions of Faces for Effective Face Recognition?** Iacopo Masi, Anh Tuan Tran, Tal Hassner, Jatuporn Toy Leksut, Gerard Medioni1 [[code]](https://github.com/iacopomasi/face_specific_augm) # NIPS 2016 **Using Fast Weights to Attend to the Recent Past** [[paper]](https://arxiv.org/abs/1610.06258) [[code]](https://github.com/ajarai/fast-weights) **Learning to learn by gradient descent by gradient descent** [[paper]](https://arxiv.org/abs/1606.04474) [[code]](https://github.com/deepmind/learning-to-learn) **R-FCN: Object Detection via Region-based Fully Convolutional Networks** [[paper]](https://arxiv.org/abs/1605.06409) [[code]](https://github.com/Orpine/py-R-FCN) **Fast and Provably Good Seedings for k-Means** [[paper]](https://las.inf.ethz.ch/files/bachem16fast.pdf) [[code]](https://github.com/obachem/kmc2) **Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences** [[paper]](https://arxiv.org/abs/1610.09513) [[code]](https://github.com/dannyneil/public_plstm) **Generative Adversarial Imitation Learning** [[paper]](https://arxiv.org/abs/1606.03476) [[code]](https://github.com/openai/imitation) **Adversarial Multiclass Classification: A Risk Minimization Perspective** [[paper]](https://www.cs.uic.edu/~rfathony/pdf/fathony2016adversarial.pdf) [[code]](https://github.com/rizalzaf/adversarial-multiclass) **Unsupervised Learning for Physical Interaction through Video Prediction** [[paper]](https://arxiv.org/abs/1605.07157) [[code]](https://github.com/tensorflow/models/tree/master/video_prediction) **Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks** [[paper]](https://arxiv.org/abs/1602.07868) [[code]](https://github.com/openai/weightnorm) **Sequential Neural Models with Stochastic Layers** [[paper]](https://arxiv.org/pdf/1605.07571.pdf) [[code]](https://github.com/marcofraccaro/srnn) **Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering** [[paper]](https://arxiv.org/abs/1606.09375) [[code]](https://github.com/mdeff/cnn_graph) **Interpretable Distribution Features with Maximum Testing Power** [[paper]](https://papers.nips.cc/paper/6148-interpretable-distribution-features-with-maximum-testing-power.pdf) [[code]](https://github.com/wittawatj/interpretable-test/) **PVANet: Lightweight Deep Neural Networks for Real-time Object Detection** [[paper]](https://arxiv.org/abs/1611.08588) [[code]](https://github.com/sanghoon/pva-faster-rcnn) **Convolutional Neural Fabrics for Architecture Learning** [[paper]](https://arxiv.org/pdf/1606.02492.pdf) [[code]](https://github.com/shreyassaxena/convolutional-neural-fabrics) **Binarized Neural Networks** [[paper]](https://arxiv.org/abs/1602.02830) [[Code]](https://github.com/MatthieuCourbariaux/BinaryNet) # What I have to read & check! **Identity Mappings in Deep Residual Networks** Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun [[code]](https://github.com/KaimingHe/resnet-1k-layers) **Is Faster R-CNN Doing Well for Pedestrian Detection?** Liliang Zhang, Liang Lin, XiaodanLiang, KaimingHe [[code]](https://github.com/zhangliliang/RPN_BF) **Do We Really Need to Collect Millions of Faces for Effective Face Recognition?** Iacopo Masi, Anh Tuan Tran, Tal Hassner, Jatuporn Toy Leksut, Gerard Medioni1 [[code]](https://github.com/iacopomasi/face_specific_augm) **A Unified Multi-scale Deep Convolutional Neural Networkfor Fast Object Detection** Zhaowei Cai, Quanfu Fan, Rogerio Feris, Nuno Vasconcelos [[code]](https://github.com/zhaoweicai/mscnn) **Using Fast Weights to Attend to the Recent Past** [[paper]](https://arxiv.org/abs/1610.06258) [[code]](https://github.com/ajarai/fast-weights) **Learning to learn by gradient descent by gradient descent** [[paper]](https://arxiv.org/abs/1606.04474) [[code]](https://github.com/deepmind/learning-to-learn)