Hao_Zhang_Vision-CSDN博客

原创 [深度学习论文笔记] Convolutional Neuron Networks and its Applications

In artificial intelligence, there exists a Moravec’s Paradox, 1 “High-level reasoning requires very little computation, but low-level sensorimotor skills require enormous computational resources”. It

2016-11-19 11:11:23 1241

原创 [机器学习基础] Notes on Machine Learning

This note was written when I was starting studying machine learning. The first part includes mathematical background such as linear algebra, probability, statistics, information theory, and numerical

2016-09-28 09:14:44 529

原创 [深度学习基础] 深度学习基础及数学原理

图像分类 (image classification) 问题是指, 假设给定一系列离散的类别(categories)(如猫, 狗, 飞机, 货车, ...), 对于给定的图像, 从这些类别中赋予一个作为它的标记 (label). 图像分类问题是计算机视觉领域的核心问题之一, 也与目标检测 (object detection), 目标分割 (object segmentation) 等其他计算机视觉

2016-09-26 20:30:31 7648 3

原创 [深度学习论文笔记][Video Classification] Delving Deeper into Convolutional Networks for Learning Video Repre

Ballas, Nicolas, et al. “Delving Deeper into Convolutional Networks for Learning Video Representations.” arXiv preprint arXiv:1511.06432 (2015). (Citaions: 14).1 MotivationPrevious works on Re

2016-11-17 16:01:17 2082

原创 [深度学习论文笔记][Video Classification] Beyond Short Snippets: Deep Networks for Video Classification

Yue-Hei Ng, Joe, et al. “Beyond short snippets: Deep networks for video classification.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. (Citations: 171).1 A

2016-11-17 14:57:26 3235

原创 [深度学习论文笔记][Video Classification] Long-term Recurrent Convolutional Networks for Visual Recognition a

Donahue, Jeffrey, et al. ”Long-term recurrent convolutional networks for visual recognition and description.” Proceedings of the IEEE Conference on Computer Vision and PatternRecognition. 2015. (Cit

2016-11-17 11:06:12 1723 1

原创 [深度学习论文笔记][Video Classification] Two-Stream Convolutional Networks for Action Recognition in Videos

Simonyan, Karen, and Andrew Zisserman. “Two-stream convolutional networks for action recognition in videos.” Advances in Neural Information Processing Systems. 2014.(Citations: 425).1 Motivati

2016-11-17 09:27:36 1932

原创 [深度学习论文笔记][Video Classification] Learning Spatiotemporal Features with 3D Convolutional Networks

Tran, Du, et al. “Learning spatiotemporal features with 3d convolutional networks.” 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. (Citations: 101).1 ArchitectureThi

2016-11-16 11:04:09 1917

原创 [深度学习论文笔记][Video Classification] Large-scale Video Classification with Convolutional Neural Networks

Karpathy, Andrej, et al. “Large-scale video classification with convolutional neural networks.” Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. 2014. (Citations: 654).

2016-11-16 10:16:32 2498

原创 [深度学习论文笔记][Attention] Spatial Transformer Networks

Jaderberg, Max, Karen Simonyan, and Andrew Zisserman. “Spatial transformer networks.” Advances in Neural Information Processing Systems. 2015. (Citations: 116).1 MotivationThe Show, Attend and

2016-11-15 22:02:11 2996

原创 [深度学习论文笔记][Attention]Show, Attend, and Tell: Neural Image Caption Generation with Visual Attention

Xu, Kelvin, et al. “Show, attend and tell: Neural image caption generation with visual attention.” arXiv preprint arXiv:1502.03044 2.3 (2015): 5. (Citations: 401).1 MotivationIn the previous i

2016-11-15 19:53:02 6046

原创 [深度学习论文笔记][Image to Sentence Generation] Deep Visual-Semantic Alignments for Generating Image Descri

Karpathy, Andrej, and Li Fei-Fei. “Deep visual-semantic alignments for generating image descriptions.” Proceedings of the IEEE Conference on Computer Vision and PatternRecognition. 2015. (Citations:

2016-11-14 21:29:18 1551

原创 [深度学习论文笔记][Recurrent Neural Networks] Visualizing and Understanding Recurrent Networks

Karpathy, Andrej, Justin Johnson, and Li Fei-Fei. “Visualizing and understanding recurrent networks” arXiv preprint arXiv:1506.02078 (2015). (Citations: 79).1 RNNRNN has formWhere W vari

2016-11-14 15:15:59 1250

原创 [深度学习论文笔记][Instance Segmentation] Instance-aware Semantic Segmentation via Multi-task Network Cascad

Dai, Jifeng, Kaiming He, and Jian Sun. “Instance-aware semantic segmentation via multitask network cascades.” arXiv preprint arXiv:1512.04412 (2015). (Citations: 40).1 MotivationAll previous w

2016-11-13 20:12:25 2063 1

原创 [深度学习论文笔记][Instance Segmentation] Hypercolumns for Object Segmentation and Fine-Grained Localization

Hariharan, Bharath, et al. “Hypercolumns for object segmentation and fine-grained localization.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recogni-tion. 2015. (Citations: 185

2016-11-13 19:01:25 3239

原创 [深度学习论文笔记][Instance Segmentation] Simultaneous Detection and Segmentation

Hariharan, Bharath, et al. “Simultaneous detection and segmentation.” European Conference on Computer Vision. Springer International Publishing, 2014. (Citations: 234).1 PipelineSee Fig. The i

2016-11-13 16:41:08 2713

原创 [深度学习论文笔记][Semantic Segmentation] Learning Deconvolution Network for Semantic Segmentation

Noh, Hyeonwoo, Seunghoon Hong, and Bohyung Han. “Learning deconvolution network for semantic segmentation.” Proceedings of the IEEE International Conference on Com-puter Vision. 2015. (Citations: 13

2016-11-13 15:41:55 1025

原创 [深度学习论文笔记][Semantic Segmentation] Fully Convolutional Networks for Semantic Segmentation

Long, Jonathan, Evan Shelhamer, and Trevor Darrell. “Fully convolutional networks for semantic segmentation.” Proceedings of the IEEE Conference on Computer Vision andPattern Recognition. 2015. (Cit

2016-11-13 14:46:12 1055

原创 [深度学习论文笔记][Semantic Segmentation] Recurrent Convolutional Neural Networks for Scene Labeling

Pinheiro, Pedro HO, and Ronan Collobert. “Recurrent Convolutional Neural Networks for Scene Labeling.” ICML. 2014. (Citations: 163).1 PipelineSee Fig. Each instance takes as input both an resi

2016-11-12 19:43:13 1383 1

原创 [深度学习论文笔记][Semantic Segmentation] Learning Hierarchical Features for Scene Labeling

Farabet, Clement, et al. “Learning hierarchical features for scene labeling.” IEEE transactions on pattern analysis and machine intelligence 35.8 (2013): 1915-1929. (Citations:703).1 Pipeline

2016-11-12 18:33:09 691

原创 [深度学习论文笔记][Object Detection] You Only Look Once: Unified, Real-Time Object Detection

Redmon, Joseph, et al. “You only look once: Unified, real-time object detection.” arXiv preprint arXiv:1506.02640 (2015). (Citations: 76).1 MotivationWe frame object detection as a regression

2016-11-12 17:57:04 820

原创 [深度学习论文笔记][Object Detection] Faster R-CNN: Towards Real-Time Object

Ren, Shaoqing, et al. “Faster R-CNN: Towards real-time object detection with region proposal networks.” Advances in neural information processing systems. 2015. (Citations:444).1 MotivationR

2016-11-10 21:14:22 984

原创 [深度学习论文笔记][Object Detection] Fast R-CNN

Girshick, Ross. “Fast r-cnn.” Proceedings of the IEEE International Conference on Computer Vision. 2015. (Citations: 444).1 R-CNN Problems• Slow at test-time: need to run full forward pass of

2016-11-10 15:16:14 622

原创 [深度学习论文笔记][Object Detection] Rich feature hierarchies for accurate object detection and semantic seg

Girshick, Ross, et al. “Rich feature hierarchies for accurate object detection and semantic segmentation.” Proceedings of the IEEE conference on computer vision and patternrecognition. 2014. (Citati

2016-11-09 18:36:32 567

原创 [深度学习论文笔记][Object Localization] OverFeat: Integrated Recognition, Localization and Detection using C

Sermanet, Pierre, et al. “Overfeat: Integrated recognition, localization and detection using convolutional networks.” arXiv preprint arXiv:1312.6229 (2013). (Citations: 978).1 MotivationObject

2016-11-08 19:56:46 1411

原创 [深度学习论文笔记][Human Pose Estimation] DeepPose: Human Pose Estimation via Deep Neural Networks

DeepPose: Human Pose Estimation via Deep Neural NetworksToshev, Alexander, and Christian Szegedy. “Deeppose: Human pose estimation via deep neural networks.” Proceedings of the IEEE Conference on Co

2016-11-08 15:29:36 1503

原创 [深度学习论文笔记][Adversarial Examples] Explaining and Harnessing Adversarial Examples

Goodfellow, Ian J., Jonathon Shlens, and Christian Szegedy. “Explaining and harnessing adversarial examples.” arXiv preprint arXiv:1412.6572 (2014). (Citations: 129).10.3.1 Fast Gradient Sign Me

2016-11-07 09:27:11 3716

原创 [深度学习论文笔记][Adversarial Examples] Deep Neural Networks are Easily Fooled: High Confidence Predictions

Nguyen, Anh, Jason Yosinski, and Jeff Clune. “Deep neural networks are easily fooled: High confidence predictions for unrecognizable images.” 2015 IEEE Conference on Com-puter Vision and Pattern Rec

2016-11-03 18:16:30 1449

原创 [深度学习论文笔记][Adversarial Examples] Intriguing properties of neural networks

Szegedy, Christian, et al. “Intriguing properties of neural networks.” arXiv preprint arXiv:1312.6199 (2013). (Citations: 251).1 Representation of High Level Neurons1.1 MotivationPreviou

2016-11-03 11:04:13 2418

原创 [深度学习论文笔记][Neural Arts] A Neural Algorithm of Artistic Style

Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. “A neural algorithm of artistic style.” arXiv preprint arXiv:1508.06576 (2015). (Citations: 99).1 MotivationGiven a content image and a

2016-11-01 09:24:14 923

原创 [深度学习论文笔记][Neural Arts] Inceptionism: Going Deeper into Neural Networks

Mordvintsev, Alexander, Christopher Olah, and Mike Tyka. “Inceptionism: Going deeper into neural networks.” Google Research Blog. Retrieved June 20 (2015). (Citations: 36).1 MotivationEach lay

2016-10-31 11:02:19 1150

原创 [深度学习论文笔记][Image Reconstruction] Understanding Deep Image Representations by Inverting Them

Mahendran, Aravindh, and Andrea Vedaldi. “Understanding deep image representations by inverting them.” 2015 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, 2015. (Citations:

2016-10-31 10:07:51 3127

原创 [深度学习论文笔记][Visualizing] Understanding Neural Networks Through Deep Visualization

Yosinski, Jason, et al. “Understanding neural networks through deep visualization.” arXiv preprint arXiv:1506.06579 (2015). (Citations: 65).1 Optimization For Any Arbitary Neuron2 Thre

2016-10-31 08:27:33 1227

原创 [深度学习论文笔记][Visualizing] 网络可视化部分论文导读

There are several ways to understanding and visualing CNN1 Visualizing ActivationsShow the activations of the network during the forward pass. It turns out that the activations usually start o

2016-10-29 10:19:07 981

原创 [深度学习论文笔记][Visualizing] Striving for Simplicity: The All Convolutional Net

Springenberg, Jost Tobias, et al. “Striving for simlicity: The all convolutional net.” arXiv preprint arXiv:1412.6806 (2014). (Citations: 121).1 Deconv Approach (Guided Backpropagation)It comb

2016-10-29 10:16:48 1678

原创 [深度学习论文笔记][Visualizing] Deep Inside Convolutional Networks Visualising Image Classification

Simonyan, Karen, Andrea Vedaldi, and Andrew Zisserman. “Deep inside convolutional networks: Visualising image classification models and saliency maps.” arXiv preprint arXiv:1312.6034 (2013). (Citation

2016-10-27 15:12:32 3592

原创 [深度学习论文笔记][Image Classification] Human Performance

Russakovsky, Olga, et al. “Imagenet large scale visual recognition challenge.” International Journal of Computer Vision 115.3 (2015): 211-252. (Citations: 1352).1 Error Both CNN and Human are Su

2016-10-27 15:07:22 511

原创 [深度学习论文笔记][Visualizing] Visualizing and Understanding Convolutional Networks

Zeiler, Matthew D., and Rob Fergus. “Visualizing and understanding convolutional networks.” European Conference on Computer Vision. Springer International Publishing, 2014.(Citations: 1207).Occl

2016-10-24 16:54:10 906

原创 [深度学习论文笔记][Depth Estimation] Predicting Depth, Surface Normals and Semantic Labels with a Common M

Eigen, David, and Rob Fergus. “Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture.” Proceedings of the IEEE International Conference on Computer

2016-10-18 17:32:35 2247

原创 [深度学习论文笔记][Depth Estimation] Depth Map Prediction from a Single Image using a Multi-Scale Deep Netw

Eigen, David, Christian Puhrsch, and Rob Fergus. “Depth map prediction from a single image using a multi-scale deep network.” Advances in neural information processing systems. 2014. (Citations: 161).

2016-10-18 08:18:24 1549

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