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https://github.com/sunshineatnoon/Paper-Collection

A track of papers I read
https://github.com/sunshineatnoon/Paper-Collection

computer-vision deep-learning papers

Last synced: 3 months ago
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A track of papers I read

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# Paper Collection - A List of Computer Vision Papers and Notes

- [Image Classification](#image-classification)

- [Popular Module](#popular-module)

- [Object Detection in Image](#object-detection-in-image)

- [Image Caption](#image-caption)

- [Image Generations](#image-generations)

- [Image and Language](#image-and-language)

- [Activation Maximization](#activation-maximization)

- [Style Transfer](#style-transfer)

- [Super Resolution](#super-resolution)

- [Image Segmentation](#image-segmentation)

- [Open Courses](#open-courses)

- [Online Books](#online-books)



### Image Classification:

Network in Network [[Paper]](https://arxiv.org/abs/1312.4400) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/NIN.md) [[Torch Code]](https://github.com/szagoruyko/cifar.torch/blob/master/models/nin.lua)

   * Lin, Min, Qiang Chen, and Shuicheng Yan. "Network in network." arXiv preprint arXiv:1312.4400 (2013).



VGG [[Paper]](https://arxiv.org/abs/1409.1556) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/VGG.md) [[Torch Code]](https://github.com/szagoruyko/cifar.torch/blob/master/models/vgg_bn_drop.lua)

   * Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).



GoogleNet [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/GoogleNet.md) [[Torch Code]](https://github.com/soumith/inception.torch/blob/master/googlenet.lua)

   * Szegedy, Christian, et al. "Going deeper with convolutions." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.



ResNet [[Paper]](https://arxiv.org/pdf/1512.03385.pdf) [[Note]]() [[Torch Code]](https://github.com/facebook/fb.resnet.torch)

   * He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.



### Popular Module

Dropout [[Paper]](http://www.jmlr.org/papers/volume15/srivastava14a.old/source/srivastava14a.pdf) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/Dropout.md)

* Srivastava, Nitish, et al. "Dropout: a simple way to prevent neural networks from overfitting." Journal of Machine Learning Research 15.1 (2014): 1929-1958.



Batch Normalization [[Paper]](https://arxiv.org/abs/1502.03167) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/BN.md)

* Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[J]. arXiv preprint arXiv:1502.03167, 2015.



### Object Detection in Image

RCNN [[Paper]](http://arxiv.org/abs/1311.2524) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/RCNN.md) [[Code]](https://github.com/rbgirshick/rcnn)

   * Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik, Rich feature hierarchies for accurate object detection and semantic segmentation



Spatial pyramid pooling in deep convolutional networks for visual recognition [[Paper]] (http://arxiv.org/abs/1406.4729) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/SPPNet.md) [[Code]](https://github.com/ShaoqingRen/SPP_net)

  * He K, Zhang X, Ren S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2015, 37(9): 1904-1916.



Fast R-CNN [[Paper]] (http://arxiv.org/pdf/1504.08083) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/Fast-RCNN.md) [[Code]](https://github.com/rbgirshick/fast-rcnn)

   * Ross Girshick, Fast R-CNN, arXiv:1504.08083.



Faster R-CNN, Microsoft Research [[Paper]] (http://arxiv.org/pdf/1506.01497) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/Faster%20R-CNN.md) [[Code]](https://github.com/ShaoqingRen/faster_rcnn) [[Python Code]](https://github.com/rbgirshick/py-faster-rcnn)

   * Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, arXiv:1506.01497.



End-to-end people detection in crowded scenes [[Paper]] (http://arxiv.org/abs/1506.04878)  [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/End-to-end-people-detection-in-crowded-scenes.md) [[Code]](https://github.com/Russell91/ReInspect)

   * Russell Stewart, Mykhaylo Andriluka, End-to-end people detection in crowded scenes, arXiv:1506.04878.



You Only Look Once: Unified, Real-Time Object Detection [[Paper]] (http://arxiv.org/abs/1506.02640) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/YOLO.md) [[Code]](http://pjreddie.com/darknet/yolo/)

   * Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, You Only Look Once: Unified, Real-Time Object Detection, arXiv:1506.02640



Adaptive Object Detection Using Adjacency and Zoom Prediction [[Paper]] (http://arxiv.org/abs/1512.07711) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/AZNet.md)

   * Lu Y, Javidi T, Lazebnik S. Adaptive Object Detection Using Adjacency and Zoom Prediction[J]. arXiv:1512.07711, 2015.



Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks [[Paper]](http://arxiv.org/abs/1512.04143) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/Inside-Outside-Net.md)

   * Sean Bell, C. Lawrence Zitnick, Kavita Bala, Ross Girshick. arXiv:1512.04143, 2015.



G-CNN: an Iterative Grid Based Object Detector [[Paper]](http://arxiv.org/abs/1512.07729v1)

   * Mahyar Najibi, Mohammad Rastegari, Larry S. Davis. arXiv:1512.07729, 2015.



Seq-NMS for Video Object Detection [[Paper]](http://arxiv.org/abs/1602.08465) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/Seq-NMS.md)

   * Wei Han, Pooya Khorrami, Tom Le Paine, Prajit Ramachandran, Mohammad Babaeizadeh, Honghui Shi, Jianan Li, Shuicheng Yan, Thomas S. Huang. Seq-NMS for Video Object Detection. arXiv preprint arXiv:1602.08465, 2016



### Image Caption



Exploring Nearest Neighbor Approaches for Image Captioning [[Paper]](http://arxiv.org/abs/1505.04467)

   * Devlin J, Gupta S, Girshick R, et al. Exploring Nearest Neighbor Approaches for Image Captioning[J]. arXiv preprint arXiv:1505.04467, 2015.



Show and Tell: A Neural Image Caption Generator [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Vinyals_Show_and_Tell_2015_CVPR_paper.html) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/ShowAndTell.md)

   * Vinyals, Oriol, et al. "Show and tell: A neural image caption generator." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.



### Image Generations:

Pixel Recurrent Neural Networks [[Paper]](https://arxiv.org/abs/1601.06759) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/pixel-rnn.md)

 * van den Oord A, Kalchbrenner N, Kavukcuoglu K. Pixel Recurrent Neural Networks[J]. arXiv preprint arXiv:1601.06759, 2016.



Variational Autoencoder [[Paper]](http://arxiv.org/abs/1312.6114) [[Note]](http://sunshineatnoon.github.io/VAE/)

   * Kingma D P, Welling M. Auto-encoding variational bayes[J]. arXiv preprint arXiv:1312.6114, 2013.



DRAW: A recurrent neural network for image generation [[Paper]](http://arxiv.org/abs/1502.04623) [[Torch Code]](https://github.com/vivanov879/draw) [[Tensorflow Code]](https://github.com/ericjang/draw) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/DRAW.md)

   * Gregor K, Danihelka I, Graves A, et al. DRAW: A recurrent neural network for image generation[J]. arXiv preprint arXiv:1502.04623, 2015.



Scribbler: Controlling Deep Image Synthesis with Sketch and Color [[Paper]](https://arxiv.org/pdf/1612.00835v2.pdf) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/scribble.md)

   * Patsorn Sangkloy, Jingwan Lu, et al. Scribbler: Controlling Deep Image Synthesis with Sketch and Color. arXiv preprint arXiv:1612.00835, 2016.



Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks [[Paper]](http://arxiv.org/abs/1511.06434)

  * Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. arXiv preprint arXiv:1511.06434, 2015.



Improved Techniques for Training GANs [[Paper]](http://arxiv.org/abs/1606.03498)

  * Salimans T, Goodfellow I, Zaremba W, et al. Improved Techniques for Training GANs[J]. arXiv preprint arXiv:1606.03498, 2016.



InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets[[Paper]](https://arxiv.org/abs/1606.03657)

  * Chen X, Duan Y, Houthooft R, et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets[J]. arXiv preprint arXiv:1606.03657, 2016.



Image-to-Image Translation with Conditional Adversarial Networks [[Paper]](https://arxiv.org/abs/1611.07004) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/pix2pix.md) [[Torch Code]](https://github.com/phillipi/pix2pix) [[Tensorflow Code]](https://github.com/yenchenlin/pix2pix-tensorflow)

  * Isola P, Zhu J Y, Zhou T, et al. Image-to-Image Translation with Conditional Adversarial Networks[J]. arXiv preprint arXiv:1611.07004, 2016.



Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts [[Paper]](https://arxiv.org/abs/1612.00215) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/AL_CGAN.md)

  * Levent Karacan, Zeynep Akata, Aykut Erdem, Erkut Erdem. Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts [J]. arXiv preprint arXiv:1612.00215, 2016.



Learning to Discover Cross-Domain Relations with Generative Adversarial Networks [[Paper]](https://arxiv.org/abs/1703.05192) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/DiscoGAN.md)

* Kim, Taeksoo, et al. "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks." arXiv preprint arXiv:1703.05192 (2017).



Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks  [[Paper]](https://arxiv.org/abs/1703.10593)  [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/cycleGAN.md)

 * Zhu J Y, Park T, Isola P, et al. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks[J]. arXiv preprint arXiv:1703.10593, 2017.



BEGAN: Boundary Equilibrium Generative Adversarial Networks [[Paper]](https://arxiv.org/abs/1703.10717) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/BEGAN.md)

 * Berthelot, David, Tom Schumm, and Luke Metz. "BEGAN: Boundary Equilibrium Generative Adversarial Networks." arXiv preprint arXiv:1703.10717 (2017).



StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks [[Paper]](https://arxiv.org/abs/1612.03242) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/StackGAN.md) [[Tensorflow Code]](https://github.com/hanzhanggit/StackGAN)

 * Zhang, Han, et al. "StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks." arXiv preprint arXiv:1612.03242 (2016).



Invertible Conditional GANs for image editing [[Paper]](https://arxiv.org/abs/1611.06355) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/documents/IcGAN.md)

* Perarnau G, van de Weijer J, Raducanu B, et al. Invertible Conditional GANs for image editing[J]. arXiv preprint arXiv:1611.06355, 2016.



Stacked Generative Adversarial Networks [[Paper]](https://arxiv.org/abs/1612.04357) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/documents/SGAN.md)

* Huang X, Li Y, Poursaeed O, et al. Stacked generative adversarial networks[J]. arXiv preprint arXiv:1612.04357, 2016.



Rotating Your Face Using Multi-task Deep Neural Network [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Yim_Rotating_Your_Face_2015_CVPR_paper.html) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/documents/Yim16.md)

* Yim J, Jung H, Yoo B I, et al. Rotating your face using multi-task deep neural network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 676-684.



### Image and Language

Learning Deep Representations of Fine-Grained Visual Descriptions [[Paper]](https://arxiv.org/abs/1605.05395) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/DS-JSE.pdf)

  * Reed, Scott, et al. "Learning deep representations of fine-grained visual descriptions." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.



### Activation Maximization

Synthesizing the preferred inputs for neurons in neural networks via deep generator networks [[Paper]](https://arxiv.org/abs/1605.09304) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/DGN_AM.md)

  * Nguyen A, Dosovitskiy A, Yosinski J, et al. Synthesizing the preferred inputs for neurons in neural networks via deep generator networks[J]. arXiv preprint arXiv:1605.09304, 2016.



### Style Transfer

A neural algorithm of artistic style [[Paper]](http://arxiv.org/abs/1508.06576) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/A%20Neural%20Algorithm%20of%20Artistic%20Style.md)

  * Gatys L A, Ecker A S, Bethge M. A neural algorithm of artistic style[J]. arXiv preprint arXiv:1508.06576, 2015.



Perceptual losses for real-time style transfer and super-resolution [[Paper]](https://arxiv.org/abs/1603.08155) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/Perceptual%20Losses%20Neural%20Style.md)

  * Johnson J, Alahi A, Fei-Fei L. Perceptual losses for real-time style transfer and super-resolution[J]. arXiv preprint arXiv:1603.08155, 2016.



Preserving Color in Neural Artistic Style Transfer [[Paper]](https://arxiv.org/abs/1606.05897) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/preserveNT.md) [[Pytorch Code]](https://github.com/sunshineatnoon/Paper-Implementations/tree/master/NeuralSytleTransfer#neural-style-transfer-with-color-preservation)

  * Gatys, Leon A., et al. "Preserving color in neural artistic style transfer." arXiv preprint arXiv:1606.05897 (2016).



A Learned Representation For Artistic Style [[Paper]](https://arxiv.org/pdf/1610.07629.pdf) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/multi-neural.md) [[Tensorflow Code]](https://github.com/tensorflow/magenta/tree/master/magenta/models/image_stylization) [[Lasagne Code]](https://github.com/joelmoniz/gogh-figure)

  * Dumoulin, Vincent, Jonathon Shlens, and Manjunath Kudlur. "A learned representation for artistic style." (2017).



Demystifying Neural Style Transfer [[Paper]](https://arxiv.org/abs/1701.01036)

  * Li, Yanghao, et al. "Demystifying Neural Style Transfer." arXiv preprint arXiv:1701.01036 (2017).



Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization [[Paper]](https://arxiv.org/abs/1703.06868)

  * Huang, Xun, and Serge Belongie. "Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization." arXiv preprint arXiv:1703.06868 (2017).



Fast Patch-based Style Transfer of Arbitrary Style [[Paper]](https://arxiv.org/pdf/1612.04337v1.pdf)

  * Chen, Tian Qi, and Mark Schmidt. "Fast Patch-based Style Transfer of Arbitrary Style." arXiv preprint arXiv:1612.04337 (2016).



### Low-level vision

Texture Enhancement via High-Resolution Style Transfer for Single-Image Super-Resolution [[Paper]](https://arxiv.org/abs/1612.00085) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/style_SR.md)

  * Il Jun Ahn, Woo Hyun Nam. Texture Enhancement via High-Resolution Style Transfer for Single-Image Super-Resolution [J]. arXiv preprint arXiv:1612.00085, 2016.



Deep Joint Image Filtering [[Paper]](https://pdfs.semanticscholar.org/9bc0/d4609fadc139480096ca95772bd82303a985.pdf) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/documents/ECCVJointFilter.md)

  * Li Y, Huang J B, Ahuja N, et al. Deep joint image filtering[C]//European Conference on Computer Vision. Springer International Publishing, 2016: 154-169.

  

### Image Segmentation  

Fully convolutional networks for semantic segmentation [[Paper]](https://arxiv.org/abs/1411.4038) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/FCN.md)

   * 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.



### Video Editing

Deep Video Color Propagation [[Paper]](https://arxiv.org/abs/1808.03232) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/documents/deepcolorprop/deepvideocolorprop.md)

  * Meyer S, Cornillère V, Djelouah A, et al. Deep Video Color Propagation. BMVC 2018.



### Deep Matching

AnchorNet: A Weakly Supervised Network to Learn Geometry-sensitive Features For Semantic Matching [[Paper]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Novotny_AnchorNet_A_Weakly_CVPR_2017_paper.pdf) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/documents/anchorNet/anchorNet.md)

  * Novotný D, Larlus D, Vedaldi A. AnchorNet: A Weakly Supervised Network to Learn Geometry-Sensitive Features for Semantic Matching, CVPR. 2017



### Open Courses

* CS231n: Convolutional Neural Networks for Visual Recognition [[Course Page]](http://vision.stanford.edu/teaching/cs231n/index.html)

* CS224d: Deep Learning for Natural Language Processing [[Course Page]](http://cs224d.stanford.edu/index.html)



### Online Books

* [Deep Learning](http://www.deeplearningbook.org) by Ian Goodfellow, Yoshua Bengio and Aaron Courville



### Mathmatics

* Introduction to Probability Models, Sheldon M. Ross



### Misc

k-means++: The advantages of careful seeding [[Paper]](http://theory.stanford.edu/~sergei/papers/kMeansPP-soda.pdf) [[Note]](https://github.com/sunshineatnoon/Paper-Collection/blob/master/k-means++.md)

   * Arthur D, Vassilvitskii S. k-means++: The advantages of careful seeding[C]//Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics, 2007: 1027-1035.