PaperList

1. Expression

• [x] [acmi 2015]Image based Static Facial Expression Recognition with Multiple Deep Network Learning.pdf
• [ ] [arXiv 1703.01210]EmotioNet Challenge-Recognition of facial expressions of emotion in the wild.pdf
• [x] [arXiv 1704.06756]Convolutional Neural Networks for Facial Expression Recognition.pdf
• [ ] [arXiv 1707.04061]Automatic Recognition of Deceptive Facial expressions of emotion.pdf
• [ ] [arXiv 1708.03985]AffectNet A Database for Facial Expression,Valence, and Arousal Computing in the Wild.pdf
• [x] [arXiv 1710.03144]Island Loss for Learning Discriminative Features in Facial Expression.pdf
• [ ] [arXiv 1711.06303]Grammatical facial expression recognition using customized.pdf
• [x] [arXiv 1711.07011]MicroExpNet An Extremely Small and Fast Model For Expression Recognition.pdf
• [ ] [arXiv 1712.00195]3D Facial Action Units Recognition for Emotional.pdf
• [ ] [arXiv 1712.01496]Learning Pain from Action Unit Combinations A Weakly Supervised Approach via Multiple.pdf
• [ ] [arXiv 1712.01661]Recognizing Gender from Human Facial Regions using Genetic Algorithm.pdf
• [ ] [arXiv 1712.03474]Geometry Guided Adversarial Facial Expression Synthesis.pdf
• [ ] [arXiv 1712.03491]3D Facial Expression Reconstruction using Cascaded Regression.pdf
• [ ] [ cvpr 2016 ]EmotioNet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild

2. Face Verification / Recognition

• [ ] [cvpr 2014 deepid]Deep Learning Face Representation from Predicting 10,000 Classes.pdf
• [ ] [bmvc 2015]Deep Face Recognition.pdf
• [ ] [arXiv 1503.03832]FaceNet A Unified Embedding for Face Recognition and Clustering.pdf
• [ ] [arXiv 1511.02683]A Light CNN for Deep Face Representation with Noisy Labels.pdf
• [ ] [eccv 2016]A Discriminative Feature Learning Approach for Deep Face Recognition.pdf
• [ ] [cvpr 2014]DeepFace Closing the Gap to Human-Level Performance in Face Verification.pdf
• [ ] [arXiv 1702.06890]Learning Deep Features via Congenerous Cosine Loss for Person Recognition.pdf
• [ ] [arXiv 1703.09507]L2-constrained Softmax Loss for Discriminative Face Verification.pdf
• [ ] [arXiv 1704.06369]normFace L2 Hypersphere Embedding fo face verfication.pdf
• [ ] [arXiv 1704.08063]SphereFace Deep Hypersphere Embedding for Face Recognition.pdf
• [ ] [arXiv 1706.04264]von Mises-Fisher Mixture Model-based Deep Learning Deep Face.pdf

3.Object Detection / Recognition / Semantic（instance） Segmentation / Tracking

• [ ] [arXiv 1411.4038 ]Fully Convolutional Networks for Semantic Segmentation.pdf
• [ ] [arXiv 1506.01497]Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• [ ] [arXiv 1510.08160]Scale-aware Fast R-CNN for Pedestrian Detection
• [ ] [arXiv 1602.07261]Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning.pdf
• [ ] [arXiv 1605.06409]R-FCN Object Detection via Region-based Fully Convolutional Networks.pdf
• [ ] [arXiv 1611.05431]Aggregated Residual Transformations for Deep Neural Networks.pdf
• [ ] [arXiv 1612.03144]Feature Pyramid Networks for Object Detection.pdf
• [ ] [arXiv 1703.06870]Mask R-CNN.pdf

4. Deep Learning Books / Paper

• [ ] [SomethingElse]CNN_book.pdf
• [ ] [arXiv 1609.04747]An overview of gradient descent optimization algorithms.pdf
• [ ] [arXiv 1611.10012]Speed-accuracy trade-offs for modern convolutional object detectors.pdf

5.Math

• [ ] [mathType]The Matrix Cookbook.pdf

6.Summary

• [x] 目标识别算法