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[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

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