深度学习在CV领域的进展以及一些由深度学习演变的新技术
CV領(lǐng)域
1.進(jìn)展：如上圖所述，當(dāng)前CV領(lǐng)域主要包括兩個大的方向，”低層次的感知” 和 “高層次的認(rèn)知”。
2.主要的應(yīng)用領(lǐng)域：視頻監(jiān)控、人臉識別、醫(yī)學(xué)圖像分析、自動駕駛、 機(jī)器人、AR、VR
3.主要的技術(shù)：分類、目標(biāo)檢測（識別)、分割、目標(biāo)追蹤、邊緣檢測、姿勢評估、理解CNN、超分辨率重建、序列學(xué)習(xí)、特征檢測與匹配、圖像標(biāo)定，視頻標(biāo)定、問答系統(tǒng)、圖片生成（文本生成圖像）、視覺關(guān)注性和顯著性（質(zhì)量評價）、人臉識別、3D重建、推薦系統(tǒng)、細(xì)粒度圖像分析、圖像壓縮
分類主要需要解決的問題是“我是誰？”
目標(biāo)檢測主要需要解決的問題是“我是誰？ 我在哪里？”
分割主要需要解決的問題是“我是誰？ 我在哪里？你是否能夠正確分割我？”
目標(biāo)追蹤主要需要解決的問題是“你能不能跟上我的步伐，盡快找到我？”
邊緣檢測主要需要解決的問題是：“如何準(zhǔn)確的檢測到目標(biāo)的邊緣？”
人體姿勢評估主要需要解決的問題是：“你需要通過我的姿勢判斷我在干什么？”
理解CNN主要需要解決的問題是：“從理論上深層次的去理解CNN的原理？”
超分辨率重建主要需要解決的問題是：“你如何從低質(zhì)量圖片獲得高質(zhì)量的圖片？”
序列學(xué)習(xí)主要解決的問題是“你知道我的下一幅圖像或者下一幀視頻是什么嗎？”
特征檢測與匹配主要需要解決的問題是“檢測圖像的特征，判斷相似程度？”
圖像標(biāo)定主要需要解決的問題是“你能說出圖像中有什么東西？他們在干什么呢？”
視頻標(biāo)定主要需要解決的問題是“你知道我這幾幀視頻說明了什么嗎？”
問答系統(tǒng)主要需要解決的問題是：“你能根據(jù)圖像正確回答我提問的問題嗎？”
圖片生成主要需要解決的問題是：“我能通過你給的信息準(zhǔn)確的生成對應(yīng)的圖片？”
視覺關(guān)注性和顯著性主要需要解決的問題是：“如何提出模擬人類視覺注意機(jī)制的模型？”
人臉識別主要需要解決的問題是：“機(jī)器如何準(zhǔn)確的識別出同一個人在不同情況下的臉？”
3D重建主要需要解決的問題是“你能通過我給你的圖片生成對應(yīng)的高質(zhì)量3D點云嗎？”
推薦系統(tǒng)主要需要解決的問題是“你能根據(jù)我的輸入給出準(zhǔn)確的輸出嗎？”
細(xì)粒度圖像分析主要需要解決的問題是“你能辨別出我是哪一種狗嗎？等這些更精細(xì)的任務(wù)”
圖像壓縮主要需要解決的問題是“如何以較少的比特有損或者無損的表示原來的圖像？”
注：
1. 以下我主要從CV領(lǐng)域中的各個小的領(lǐng)域入手，總結(jié)該領(lǐng)域中一些網(wǎng)絡(luò)模型，基本上覆蓋到了各個領(lǐng)域，力求完整的收集各種經(jīng)典的模型，順序基本上是按照時間的先后，一般最后是該領(lǐng)域最新提出來的方案，我主要的目的是做一個整理，方便自己和他人的使用，你不再需要去網(wǎng)上收集大把的資料，需要的是仔細(xì)分析這些模型，并提出自己新的模型。這里面收集的論文質(zhì)量都比較高，主要來自于ECCV、ICCV、CVPR、PAM、arxiv、ICLR、ACM等頂尖國際會議。并且為每篇論文都添加了鏈接。可以大大地節(jié)約你的時間。同時，我挑選出論文比較重要的網(wǎng)絡(luò)模型或者整體架構(gòu)，可以方便你去進(jìn)行對比。有一個更好的全局觀。具體 細(xì)節(jié)需要你去仔細(xì)的閱讀論文。由于個人的精力有限，我只能做成這樣，希望大家能夠理解。謝謝。
2. 我會利用自己的業(yè)余時間來更新新的模型，但是由于時間和精力有限，可能并不完整，我希望大家都能貢獻(xiàn)的一份力量，如果你發(fā)現(xiàn)新的模型，可以聯(lián)系我，我會及時回復(fù)大家，期待著的加入，讓我們一起服務(wù)大家！
如下圖所示：
分類：這是一個基礎(chǔ)的研究課題，已經(jīng)獲得了很高的準(zhǔn)確率，在一些場合上面已經(jīng)遠(yuǎn)遠(yuǎn)地超過啦人類啦！
典型的網(wǎng)絡(luò)模型
LeNet
http://yann.lecun.com/exdb/lenet/index.html
AlexNet
http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
https://arxiv.org/pdf/1502.01852.pdf
Batch Normalization
https://arxiv.org/pdf/1502.03167.pdf
GoogLeNet
http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf
VGGNet
https://arxiv.org/pdf/1409.1556.pdf
ResNet
https://arxiv.org/pdf/1512.03385.pdf
InceptionV4（Inception-ResNet）
https://arxiv.org/pdf/1602.07261.pdf
LeNet網(wǎng)絡(luò)1：
LeNet網(wǎng)絡(luò)2：
AlexNet網(wǎng)絡(luò)1：
AlexNet網(wǎng)絡(luò)2：
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification網(wǎng)絡(luò)：
GoogLeNet網(wǎng)絡(luò)1：
GoogLeNet網(wǎng)絡(luò)2：
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification網(wǎng)絡(luò)：
Batch Normalization：
VGGNet網(wǎng)絡(luò)1：
VGGNet網(wǎng)絡(luò)2：
ResNet網(wǎng)絡(luò)：
InceptionV4網(wǎng)絡(luò)：
圖像檢測：這是基于圖像分類的基礎(chǔ)上所做的一些研究，即分類+定位。
典型網(wǎng)絡(luò)
OVerfeat
https://arxiv.org/pdf/1312.6229.pdf
RNN
https://arxiv.org/pdf/1311.2524.pdf
SPP-Net
https://arxiv.org/pdf/1406.4729.pdf
DeepID-Net
https://arxiv.org/pdf/1409.3505.pdf
Fast R-CNN
https://arxiv.org/pdf/1504.08083.pdf
R-CNN minus R
https://arxiv.org/pdf/1506.06981.pdf
End-to-end people detection in crowded scenes
https://arxiv.org/pdf/1506.04878.pdf
DeepBox
https://arxiv.org/pdf/1505.02146.pdf
MR-CNN
http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Gidaris_Object_Detection_via_ICCV_2015_paper.pdf
Faster R-CNN
https://arxiv.org/pdf/1506.01497.pdf
YOLO
https://arxiv.org/pdf/1506.02640.pdf
DenseBox
https://arxiv.org/pdf/1509.04874.pdf
Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning
https://arxiv.org/pdf/1503.00949.pdf
R-FCN
https://arxiv.org/pdf/1605.06409.pdf
SSD
https://arxiv.org/pdf/1512.02325v2.pdf
Inside-Outside Net
https://arxiv.org/pdf/1512.04143.pdf
G-CNN
https://arxiv.org/pdf/1512.07729.pdf
PVANET
https://arxiv.org/pdf/1608.08021.pdf
Speed/accuracy trade-offs for modern convolutional object detectors
https://arxiv.org/pdf/1611.10012v1.pdf
OVerfeat網(wǎng)絡(luò)：
R-CNN網(wǎng)絡(luò)：
SPP-Net網(wǎng)絡(luò)：
DeepID-Net網(wǎng)絡(luò)：
DeepBox網(wǎng)絡(luò)：
MR-CNN網(wǎng)絡(luò)：
Fast-RCNN網(wǎng)絡(luò)：
R-CNN minus R網(wǎng)絡(luò)：
End-to-end people detection in crowded scenes網(wǎng)絡(luò)：
Faster-RCNN網(wǎng)絡(luò)：
DenseBox網(wǎng)絡(luò)：
Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning網(wǎng)絡(luò)：
R-FCN網(wǎng)絡(luò)：
YOLO和SDD網(wǎng)絡(luò)：
Inside-Outside Net網(wǎng)絡(luò)：
G-CNN網(wǎng)絡(luò)：
PVANET網(wǎng)絡(luò)：
Speed/accuracy trade-offs for modern convolutional object detectors：
圖像分割
經(jīng)典網(wǎng)絡(luò)模型：
FCN
https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf
segNet
https://arxiv.org/pdf/1511.00561.pdf
Deeplab
https://arxiv.org/pdf/1606.00915.pdf
deconvNet
https://arxiv.org/pdf/1505.04366.pdf
Conditional Random Fields as Recurrent Neural Networks
http://www.robots.ox.ac.uk/~szheng/papers/CRFasRNN.pdf
Semantic Segmentation using Adversarial Networks
https://arxiv.org/pdf/1611.08408.pdf
SEC: Seed, Expand and Constrain：
http://pub.ist.ac.at/~akolesnikov/files/ECCV2016/main.pdf
Efficient piecewise training of deep structured models for semantic segmentation
https://arxiv.org/pdf/1504.01013.pdf
Semantic Image Segmentation via Deep Parsing Network
https://arxiv.org/pdf/1509.02634.pdf
BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation
https://arxiv.org/pdf/1503.01640.pdf
Learning Deconvolution Network for Semantic Segmentation
https://arxiv.org/pdf/1505.04366.pdf
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
https://arxiv.org/pdf/1506.04924.pdf
PUSHING THE BOUNDARIES OF BOUNDARY DETECTION USING DEEP LEARNING
https://arxiv.org/pdf/1511.07386.pdf
Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network
https://arxiv.org/pdf/1512.07928.pdf
Feedforward Semantic Segmentation With Zoom-Out Features
http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Mostajabi_Feedforward_Semantic_Segmentation_2015_CVPR_paper.pdf
Joint Calibration for Semantic Segmentation
https://arxiv.org/pdf/1507.01581.pdf
Hypercolumns for Object Segmentation and Fine-Grained Localization
http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Hariharan_Hypercolumns_for_Object_2015_CVPR_paper.pdf
Scene Parsing with Multiscale Feature Learning
http://yann.lecun.com/exdb/publis/pdf/farabet-icml-12.pdf
Learning Hierarchical Features for Scene Labeling
http://yann.lecun.com/exdb/publis/pdf/farabet-pami-13.pdf
Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing
http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Izadinia_Segment-Phrase_Table_for_ICCV_2015_paper.pdf
MULTI-SCALE CONTEXT AGGREGATION BY DILATED CONVOLUTIONS
https://arxiv.org/pdf/1511.07122v2.pdf
Weakly supervised graph based semantic segmentation by learning communities of image-parts
http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Pourian_Weakly_Supervised_Graph_ICCV_2015_paper.pdf
FCN網(wǎng)絡(luò)1：
FCN網(wǎng)絡(luò)2：
segNet網(wǎng)絡(luò)：
Deeplab網(wǎng)絡(luò)：
deconvNet網(wǎng)絡(luò)：
Conditional Random Fields as Recurrent Neural Networks網(wǎng)絡(luò)：
Semantic Segmentation using Adversarial Networks網(wǎng)絡(luò)：
SEC: Seed, Expand and Constrain網(wǎng)絡(luò)：
Efficient piecewise training of deep structured models for semantic segmentation網(wǎng)絡(luò)：
Semantic Image Segmentation via Deep Parsing Network網(wǎng)絡(luò)：
BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation：
Learning Deconvolution Network for Semantic Segmentation：
PUSHING THE BOUNDARIES OF BOUNDARY DETECTION USING DEEP LEARNING：
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation：
Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network：
Feedforward Semantic Segmentation With Zoom-Out Features網(wǎng)絡(luò)：
Joint Calibration for Semantic Segmentation：
Hypercolumns for Object Segmentation and Fine-Grained Localization：
Learning Hierarchical Features for Scene Labeling：
MULTI-SCALE CONTEXT AGGREGATION BY DILATED CONVOLUTIONS：
Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing：
Weakly supervised graph based semantic segmentation by learning communities of image-parts：
Scene Parsing with Multiscale Feature Learning：
目標(biāo)追蹤
經(jīng)典網(wǎng)絡(luò)：
DLT
https://pdfs.semanticscholar.org/b218/0fc4f5cb46b5b5394487842399c501381d67.pdf
Transferring Rich Feature Hierarchies for Robust Visual Tracking
https://arxiv.org/pdf/1501.04587.pdf
FCNT
http://202.118.75.4/lu/Paper/ICCV2015/iccv15_lijun.pdf
Hierarchical Convolutional Features for Visual Tracking
http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Ma_Hierarchical_Convolutional_Features_ICCV_2015_paper.pdf
MDNet
https://arxiv.org/pdf/1510.07945.pdf
Recurrently Target-Attending Tracking
http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Cui_Recurrently_Target-Attending_Tracking_CVPR_2016_paper.pdf
DeepTracking
http://www.bmva.org/bmvc/2014/files/paper028.pdf
DeepTrack
http://www.bmva.org/bmvc/2014/files/paper028.pdf
Online Tracking by Learning Discriminative Saliency Map
with Convolutional Neural Network
https://arxiv.org/pdf/1502.06796.pdf
Transferring Rich Feature Hierarchies for Robust Visual Tracking
https://arxiv.org/pdf/1501.04587.pdf
DLT網(wǎng)絡(luò)：
Transferring Rich Feature Hierarchies for Robust Visual Tracking網(wǎng)絡(luò)：
FCNT網(wǎng)絡(luò)：
Hierarchical Convolutional Features for Visual Tracking網(wǎng)絡(luò)：
MDNet網(wǎng)絡(luò)：
DeepTracking網(wǎng)絡(luò)：
ecurrently Target-Attending Tracking網(wǎng)絡(luò)：
DeepTrack網(wǎng)絡(luò)：
Online Tracking by Learning Discriminative Saliency Map
with Convolutional Neural Network：
邊緣檢測
經(jīng)典模型：
HED
https://arxiv.org/pdf/1504.06375.pdf
DeepEdge
https://arxiv.org/pdf/1412.1123.pdf
DeepConto
http://mc.eistar.net/UpLoadFiles/Papers/DeepContour_cvpr15.pdf
HED網(wǎng)絡(luò)：
DeepEdge網(wǎng)絡(luò)：
DeepContour網(wǎng)絡(luò)：
人體姿勢評估
經(jīng)典模型：
DeepPose
https://arxiv.org/pdf/1312.4659.pdf
JTCN
https://www.robots.ox.ac.uk/~vgg/rg/papers/tompson2014.pdf
Flowing convnets for human pose estimation in videos
https://arxiv.org/pdf/1506.02897.pdf
Stacked hourglass networks for human pose estimation
https://arxiv.org/pdf/1603.06937.pdf
Convolutional pose machines
https://arxiv.org/pdf/1602.00134.pdf
Deepcut
https://arxiv.org/pdf/1605.03170.pdf
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://arxiv.org/pdf/1611.08050.pdf
DeepPose網(wǎng)絡(luò)：
JTCN網(wǎng)絡(luò)：
Flowing convnets for human pose estimation in videos網(wǎng)絡(luò)：
Stacked hourglass networks for human pose estimation網(wǎng)絡(luò)：
Convolutional pose machines網(wǎng)絡(luò)：
Deepcut網(wǎng)絡(luò)：
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields網(wǎng)絡(luò)：
理解CNN
經(jīng)典網(wǎng)絡(luò)：
Visualizing and Understanding Convolutional Networks
https://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf
Inverting Visual Representations with Convolutional Networks
https://arxiv.org/pdf/1506.02753.pdf
Object Detectors Emerge in Deep Scene CNNs
https://arxiv.org/pdf/1412.6856.pdf
Understanding Deep Image Representations by Inverting Them
http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Mahendran_Understanding_Deep_Image_2015_CVPR_paper.pdf
Deep Neural Networks are Easily Fooled:High Confidence Predictions for Unrecognizable Images
http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Nguyen_Deep_Neural_Networks_2015_CVPR_paper.pdf
Understanding image representations by measuring their equivariance and equivalence
http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Lenc_Understanding_Image_Representations_2015_CVPR_paper.pdf
Visualizing and Understanding Convolutional Networks網(wǎng)絡(luò)：
Inverting Visual Representations with Convolutional Networks：
Object Detectors Emerge in Deep Scene CNNs：
Understanding Deep Image Representations by Inverting Them：
Deep Neural Networks are Easily Fooled:High Confidence Predictions for Unrecognizable Images：
Understanding image representations by measuring their equivariance and equivalence：
超分辨率重建
經(jīng)典模型：
Learning Iterative Image Reconstruction
http://www.ais.uni-bonn.de/behnke/papers/ijcai01.pdf
Learning Iterative Image Reconstruction in the Neural Abstraction Pyramid
http://www.ais.uni-bonn.de/behnke/papers/ijcia01.pdf
Learning a Deep Convolutional Network for Image Super-Resolution
http://personal.ie.cuhk.edu.hk/~ccloy/files/eccv_2014_deepresolution.pdf
Image Super-Resolution Using Deep Convolutional Networks
https://arxiv.org/pdf/1501.00092.pdf
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
https://arxiv.org/pdf/1511.04587.pdf
Deeply-Recursive Convolutional Network for Image Super-Resolution
https://arxiv.org/pdf/1511.04491.pdf
Deep Networks for Image Super-Resolution with Sparse Prior
http://www.ifp.illinois.edu/~dingliu2/iccv15/iccv15.pdf
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://arxiv.org/pdf/1603.08155.pdf
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://arxiv.org/pdf/1609.04802v3.pdf
Learning Iterative Image Reconstruction網(wǎng)絡(luò)：
Learning Iterative Image Reconstruction in the Neural Abstraction Pyramid：
Learning a Deep Convolutional Network for Image Super-Resolution：
Image Super-Resolution Using Deep Convolutional Networks：
Accurate Image Super-Resolution Using Very Deep Convolutional Networks：
Deeply-Recursive Convolutional Network for Image Super-Resolution：
Deep Networks for Image Super-Resolution with Sparse Prior：
Perceptual Losses for Real-Time Style Transfer and Super-Resolution：
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network：
圖像標(biāo)定
經(jīng)典模型：
Explain Images with Multimodal Recurrent Neural Networks
https://arxiv.org/pdf/1410.1090.pdf
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
https://arxiv.org/pdf/1411.2539.pdf
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
https://arxiv.org/pdf/1411.4389.pdf
A Neural Image Caption Generator
https://arxiv.org/pdf/1411.4555.pdf
Deep Visual-Semantic Alignments for Generating Image Description
http://cs.stanford.edu/people/karpathy/cvpr2015.pdf
Translating Videos to Natural Language Using Deep Recurrent Neural Networks
https://arxiv.org/pdf/1412.4729.pdf
Learning a Recurrent Visual Representation for Image Caption Generation
https://arxiv.org/pdf/1411.5654.pdf
From Captions to Visual Concepts and Back
https://arxiv.org/pdf/1411.4952.pdf
Show, Attend, and Tell: Neural Image Caption Generation with Visual Attention
http://www.cs.toronto.edu/~zemel/documents/captionAttn.pdf
Phrase-based Image Captioning
https://arxiv.org/pdf/1502.03671.pdf
Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images
https://arxiv.org/pdf/1504.06692.pdf
Exploring Nearest Neighbor Approaches for Image Captioning
https://arxiv.org/pdf/1505.04467.pdf
Image Captioning with an Intermediate Attributes Layer
https://arxiv.org/pdf/1506.01144.pdf
Learning language through pictures
https://arxiv.org/pdf/1506.03694.pdf
Describing Multimedia Content using Attention-based Encoder-Decoder Networks
https://arxiv.org/pdf/1507.01053.pdf
Image Representations and New Domains in Neural Image Captioning
https://arxiv.org/pdf/1508.02091.pdf
Learning Query and Image Similarities with Ranking Canonical Correlation Analysis
http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Yao_Learning_Query_and_ICCV_2015_paper.pdf
Generative Adversarial Text to Image Synthesis
https://arxiv.org/pdf/1605.05396.pdf
GENERATING IMAGES FROM CAPTIONS WITH ATTENTION
https://arxiv.org/pdf/1511.02793.pdf
Explain Images with Multimodal Recurrent Neural Networks：
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models：
Long-term Recurrent Convolutional Networks for Visual Recognition and Description：
A Neural Image Caption Generator：
Deep Visual-Semantic Alignments for Generating Image Description：
Translating Videos to Natural Language Using Deep Recurrent Neural Networks：
Learning a Recurrent Visual Representation for Image Caption Generation：
From Captions to Visual Concepts and Back：
Show, Attend, and Tell: Neural Image Caption Generation with Visual Attention：
Phrase-based Image Captioning：
Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images：
Exploring Nearest Neighbor Approaches for Image Captioning：
Image Captioning with an Intermediate Attributes Layer：
Learning language through pictures：
Describing Multimedia Content using Attention-based Encoder-Decoder Networks：
Image Representations and New Domains in Neural Image Captioning：
Learning Query and Image Similarities with Ranking Canonical Correlation Analysis：
Generative Adversarial Text to Image Synthesis：
GENERATING IMAGES FROM CAPTIONS WITH ATTENTION：
視頻標(biāo)注
經(jīng)典模型：
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
https://arxiv.org/pdf/1411.4389.pdf
Translating Videos to Natural Language Using Deep Recurrent Neural Networks
https://arxiv.org/pdf/1412.4729.pdf
Joint Modeling Embedding and Translation to Bridge Video and Language
https://arxiv.org/pdf/1505.01861.pdf
Sequence to Sequence–Video to Text
https://arxiv.org/pdf/1505.00487.pdf
Describing Videos by Exploiting Temporal Structure
https://arxiv.org/pdf/1502.08029.pdf
The Long-Short Story of Movie Description
https://arxiv.org/pdf/1506.01698.pdf
Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books
https://arxiv.org/pdf/1506.06724.pdf
Describing Multimedia Content using Attention-based Encoder-Decoder Networks
https://arxiv.org/pdf/1507.01053.pdf
Temporal Tessellation for Video Annotation and Summarization
https://arxiv.org/pdf/1612.06950.pdf
Summarization-based Video Caption via Deep Neural Networks
acm=1492135731_7c7cb5d6bf7455db7f4aa75b341d1a78”>http://delivery.acm.org/10.1145/2810000/2806314/p1191-li.pdf?ip=123.138.79.12&id=2806314&acc=ACTIVE%20SERVICE&key=BF85BBA5741FDC6E%2EB37B3B2DF215A17D%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=923677366&CFTOKEN=37844144&acm=1492135731_7c7cb5d6bf7455db7f4aa75b341d1a78
Deep Learning for Video Classification and Captioning
https://arxiv.org/pdf/1609.06782.pdf
Long-term Recurrent Convolutional Networks for Visual Recognition and Description：
Translating Videos to Natural Language Using Deep Recurrent Neural Networks：
Joint Modeling Embedding and Translation to Bridge Video and Language：
Sequence to Sequence–Video to Text：
Describing Videos by Exploiting Temporal Structure:
The Long-Short Story of Movie Description：
Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books：
Describing Multimedia Content using Attention-based Encoder-Decoder Networks：
Temporal Tessellation for Video Annotation and Summarization：
Summarization-based Video Caption via Deep Neural Networks：
Deep Learning for Video Classification and Captioning：
問答系統(tǒng)
經(jīng)典模型：
VQA: Visual Question Answering
https://arxiv.org/pdf/1505.00468.pdf
Ask Your Neurons: A Neural-based Approach to Answering Questions about Images
https://arxiv.org/pdf/1505.01121.pdf
Image Question Answering: A Visual Semantic Embedding Model and a New Dataset
https://arxiv.org/pdf/1505.02074.pdf
Stacked Attention Networks for Image Question Answering
https://arxiv.org/pdf/1511.02274v2.pdf
Dataset and Methods for Multilingual Image Question Answering
https://arxiv.org/pdf/1505.05612.pdf
Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction
Dynamic Memory Networks for Visual and Textual Question Answering
https://arxiv.org/pdf/1603.01417v1.pdf
Multimodal Residual Learning for Visual QA
https://arxiv.org/pdf/1606.01455.pdf
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
https://arxiv.org/pdf/1606.01847.pdf
Training Recurrent Answering Units with Joint Loss Minimization for VQA
https://arxiv.org/pdf/1606.03647.pdf
Hadamard Product for Low-rank Bilinear Pooling
https://arxiv.org/pdf/1610.04325.pdf
Question Answering Using Deep Learning
https://cs224d.stanford.edu/reports/StrohMathur.pdf
VQA: Visual Question Answering：
Ask Your Neurons: A Neural-based Approach to Answering Questions about Images：
Image Question Answering: A Visual Semantic Embedding Model and a New Dataset：
Stacked Attention Networks for Image Question Answering：
Dataset and Methods for Multilingual Image Question Answering：
Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction：
Dynamic Memory Networks for Visual and Textual Question Answering：
Multimodal Residual Learning for Visual QA：
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding：
Training Recurrent Answering Units with Joint Loss Minimization for VQA：
Hadamard Product for Low-rank Bilinear Pooling：
Question Answering Using Deep Learning：
圖片生成（CNN、RNN、LSTM、GAN）
經(jīng)典模型：
Conditional Image Generation with PixelCNN Decoders
https://arxiv.org/pdf/1606.05328v2.pdf
Learning to Generate Chairs with Convolutional Neural Networks
http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Dosovitskiy_Learning_to_Generate_2015_CVPR_paper.pdf
DRAW: A Recurrent Neural Network For Image Generation
https://arxiv.org/pdf/1502.04623v2.pdf
Generative Adversarial Networks
https://arxiv.org/pdf/1406.2661.pdf
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
https://arxiv.org/pdf/1506.05751.pdf
A note on the evaluation of generative models
https://arxiv.org/pdf/1511.01844.pdf
Variationally Auto-Encoded Deep Gaussian Processes
https://arxiv.org/pdf/1511.06455v2.pdf
Generating Images from Captions with Attention
https://arxiv.org/pdf/1511.02793v2.pdf
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
https://arxiv.org/pdf/1511.06390v1.pdf
Censoring Representations with an Adversary
https://arxiv.org/pdf/1511.05897v3.pdf
Distributional Smoothing with Virtual Adversarial Training
https://arxiv.org/pdf/1507.00677v8.pdf
Generative Visual Manipulation on the Natural Image Manifold
https://arxiv.org/pdf/1609.03552v2.pdf
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://arxiv.org/pdf/1511.06434.pdf
Wasserstein GAN
https://arxiv.org/pdf/1701.07875.pdf
Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities
https://arxiv.org/pdf/1701.06264.pdf
Conditional Generative Adversarial Nets
https://arxiv.org/pdf/1411.1784.pdf
InfoGAN: Interpretable Representation Learning byInformation Maximizing Generative Adversarial Nets
https://arxiv.org/pdf/1606.03657.pdf
Conditional Image Synthesis With Auxiliary Classifier GANs
https://arxiv.org/pdf/1610.09585.pdf
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
https://arxiv.org/pdf/1609.05473.pdf
Improved Training of Wasserstein GANs
https://arxiv.org/pdf/1704.00028.pdf
Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis
https://arxiv.org/pdf/1704.04086.pdf
Conditional Image Generation with PixelCNN Decoders：
Learning to Generate Chairs with Convolutional Neural Networks：
DRAW: A Recurrent Neural Network For Image Generation：
Generative Adversarial Networks：
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks：
A note on the evaluation of generative models：
Variationally Auto-Encoded Deep Gaussian Processes：
Generating Images from Captions with Attention：
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks：
Censoring Representations with an Adversary：
Distributional Smoothing with Virtual Adversarial Training：
Generative Visual Manipulation on the Natural Image Manifold：
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks：
Wasserstein GAN：
Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities：
Conditional Generative Adversarial Nets：
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets：
Conditional Image Synthesis With Auxiliary Classifier GANs：
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient：
Improved Training of Wasserstein GANs：
Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis：
視覺關(guān)注性和顯著性
經(jīng)典模型：
Predicting Eye Fixations using Convolutional Neural Networks
http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Liu_Predicting_Eye_Fixations_2015_CVPR_paper.pdf
Learning a Sequential Search for Landmarks
http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Singh_Learning_a_Sequential_2015_CVPR_paper.pdf
Multiple Object Recognition with Visual Attention
https://arxiv.org/pdf/1412.7755.pdf
Recurrent Models of Visual Attention
http://papers.nips.cc/paper/5542-recurrent-models-of-visual-attention.pdf
Capacity Visual Attention Networks
http://easychair.org/publications/download/Capacity_Visual_Attention_Networks
Fully Convolutional Attention Networks for Fine-Grained Recognition
https://arxiv.org/pdf/1603.06765.pdf
Predicting Eye Fixations using Convolutional Neural Networks：
Learning a Sequential Search for Landmarks：
Multiple Object Recognition with Visual Attention：
Recurrent Models of Visual Attention：
Capacity Visual Attention Networks：
Fully Convolutional Attention Networks for Fine-Grained Recognition：
特征檢測與匹配（塊）
經(jīng)典模型：
TILDE: A Temporally Invariant Learned DEtector
https://arxiv.org/pdf/1411.4568.pdf
MatchNet: Unifying Feature and Metric Learning for Patch-Based Matching
https://pdfs.semanticscholar.org/81b9/24da33b9500a2477532fd53f01df00113972.pdf
Discriminative Learning of Deep Convolutional Feature Point Descriptors
http://cvlabwww.epfl.ch/~trulls/pdf/iccv-2015-deepdesc.pdf
Learning to Assign Orientations to Feature Points
https://arxiv.org/pdf/1511.04273.pdf
PN-Net: Conjoined Triple Deep Network for Learning Local Image Descriptors
https://arxiv.org/pdf/1601.05030.pdf
Multi-scale Pyramid Pooling for Deep Convolutional Representation
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7301274
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
https://arxiv.org/pdf/1406.4729.pdf
Learning to Compare Image Patches via Convolutional Neural Networks
https://arxiv.org/pdf/1504.03641.pdf
PixelNet: Representation of the pixels, by the pixels, and for the pixels
http://www.cs.cmu.edu/~aayushb/pixelNet/pixelnet.pdf
LIFT: Learned Invariant Feature Transform
https://arxiv.org/pdf/1603.09114.pdf
TILDE: A Temporally Invariant Learned DEtector：
MatchNet: Unifying Feature and Metric Learning for Patch-Based Matching：
Discriminative Learning of Deep Convolutional Feature Point Descriptors：
Learning to Assign Orientations to Feature Points：
PN-Net: Conjoined Triple Deep Network for Learning Local Image Descriptors：
Multi-scale Pyramid Pooling for Deep Convolutional Representation：
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition：
Learning to Compare Image Patches via Convolutional Neural Networks：
PixelNet: Representation of the pixels, by the pixels, and for the pixels：
LIFT: Learned Invariant Feature Transform：
人臉識別
經(jīng)典模型：
Learning Hierarchical Representations for Face Verification with Convolutional Deep Belief Networks
http://vis-www.cs.umass.edu/papers/HuangCVPR12.pdf
Deep Convolutional Network Cascade for Facial Point Detection
http://mmlab.ie.cuhk.edu.hk/archive/CNN/data/CNN_FacePoint.pdf
Deep Nonlinear Metric Learning with Independent Subspace Analysis for Face Verification
acm=1492152722_04e9cce5378080a18ec7e700dfb4cd28”>http://delivery.acm.org/10.1145/2400000/2396303/p749-cai.pdf?ip=123.138.79.12&id=2396303&acc=ACTIVE%20SERVICE&key=BF85BBA5741FDC6E%2EB37B3B2DF215A17D%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=923677366&CFTOKEN=37844144&acm=1492152722_04e9cce5378080a18ec7e700dfb4cd28
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf
Deep learning face representation by joint identification-verification
https://arxiv.org/pdf/1406.4773.pdf
Deep learning face representation from predicting 10,000 classes
http://mmlab.ie.cuhk.edu.hk/pdf/YiSun_CVPR14.pdf
Deeply learned face representations are sparse, selective, and robust
https://arxiv.org/pdf/1412.1265.pdf
Deepid3: Face recognition with very deep neural networks
https://arxiv.org/pdf/1502.00873.pdf
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://arxiv.org/pdf/1503.03832.pdf
Funnel-Structured Cascade for Multi-View Face Detection with Alignment-Awareness
https://arxiv.org/pdf/1609.07304.pdf
Large-pose Face Alignment via CNN-based Dense 3D Model Fitting
http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Jourabloo_Large-Pose_Face_Alignment_CVPR_2016_paper.pdf
Unconstrained 3D face reconstruction
http://cvlab.cse.msu.edu/pdfs/Roth_Tong_Liu_CVPR2015.pdf
Adaptive contour fitting for pose-invariant 3D face shape reconstruction
http://akme-a2.iosb.fraunhofer.de/ETGS15p/2015_Adaptive%20contour%20fitting%20for%20pose-invariant%203D%20face%20shape%20reconstruction.pdf
High-fidelity pose and expression normalization for face recognition in the wild
http://www.cbsr.ia.ac.cn/users/xiangyuzhu/papers/CVPR2015_High-Fidelity.pdf
Adaptive 3D face reconstruction from unconstrained photo collections
http://cvlab.cse.msu.edu/pdfs/Roth_Tong_Liu_CVPR16.pdf
Dense 3D face alignment from 2d videos in real-time
http://ieeexplore.ieee.org/stamp/stamp.jsp arnumber=7163142
Robust facial landmark detection under significant head poses and occlusion
http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Wu_Robust_Facial_Landmark_ICCV_2015_paper.pdf
A convolutional neural network cascade for face detection
http://users.eecs.northwestern.edu/~xsh835/assets/cvpr2015_cascnn.pdf
Deep Face Recognition Using Deep Convolutional Neural
Network
http://aiehive.com/deep-face-recognition-using-deep-convolution-neural-network/
Multi-view Face Detection Using Deep Convolutional Neural Networks
acm=1492157015_8ffa84e6632810ea05ff005794fed8d5”>http://delivery.acm.org/10.1145/2750000/2749408/p643-farfade.pdf?ip=123.138.79.12&id=2749408&acc=ACTIVE%20SERVICE&key=BF85BBA5741FDC6E%2EB37B3B2DF215A17D%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=923677366&CFTOKEN=37844144&acm=1492157015_8ffa84e6632810ea05ff005794fed8d5
HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition
https://arxiv.org/pdf/1603.01249.pdf
Wider face: A face detectionbenchmark
http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/support/paper.pdf
Joint training of cascaded cnn for face detection
http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Qin_Joint_Training_of_CVPR_2016_paper.pdf
Face detection with end-to-end integration of a convnet and a 3d model
https://arxiv.org/pdf/1606.00850.pdf
Face Detection using Deep Learning: An Improved Faster RCNN Approach
https://arxiv.org/pdf/1701.08289.pdf
新舊方法對比：
Learning Hierarchical Representations for Face Verification with Convolutional Deep Belief Networks：
Deep Convolutional Network Cascade for Facial Point Detection：
Deep Nonlinear Metric Learning with Independent Subspace Analysis for Face Verification：
DeepFace: Closing the Gap to Human-Level Performance in Face Verification：
Deep learning face representation by joint identification-verification：
Deep learning face representation from predicting 10,000 classes：
Deeply learned face representations are sparse, selective, and robust：
Deepid3: Face recognition with very deep neural networks：
FaceNet: A Unified Embedding for Face Recognition and Clustering：
Funnel-Structured Cascade for Multi-View Face Detection with Alignment-Awareness：
Large-pose Face Alignment via CNN-based Dense 3D Model Fitting：
Unconstrained 3D face reconstruction：
Adaptive contour fitting for pose-invariant 3D face shape reconstruction：
High-fidelity pose and expression normalization for face recognition in the wild：
Adaptive 3D face reconstruction from unconstrained photo collections：
Regressing a 3D face shape from a single image：
Dense 3D face alignment from 2d videos in real-time：
Robust facial landmark detection under significant head poses and occlusion：
A convolutional neural network cascade for face detection：
Deep Face Recognition Using Deep Convolutional Neural
Network：
Multi-view Face Detection Using Deep Convolutional Neural Networks：
HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender
Recognition：
Wider face: A face detectionbenchmark
Joint training of cascaded cnn for face detection：：
Face detection with end-to-end integration of a convnet and a 3d model：
Face Detection using Deep Learning: An Improved Faster RCNN Approach：
3D重建
經(jīng)典模型：
3D ShapeNets: A Deep Representation for Volumetric Shapes
https://people.csail.mit.edu/khosla/papers/cvpr2015_wu.pdf
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
https://arxiv.org/pdf/1604.00449.pdf
Learning to generate chairs with convolutional neural networks
https://arxiv.org/pdf/1411.5928.pdf
Category-specific object reconstruction from a single image
http://people.eecs.berkeley.edu/~akar/categoryshapes.pdf
Enriching Object Detection with 2D-3D Registration and Continuous Viewpoint Estimation
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7298866
ShapeNet: An Information-Rich 3D Model Repository
https://arxiv.org/pdf/1512.03012.pdf
3D reconstruction of synapses with deep learning based on EM Images
http://ieeexplore.ieee.org/stamp/stamp.jsp？arnumber=7558866
Analysis and synthesis of 3d shape families via deep-learned generative models of surfaces
https://arxiv.org/pdf/1605.06240.pdf
Unsupervised Learning of 3D Structure from Images
https://arxiv.org/pdf/1607.00662.pdf
Deep learning 3d shape surfaces using geometry images
http://download.springer.com/static/pdf/605/chp%253A10.1007%252F978-3-319-46466-4_14.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fchapter%2F10.1007%2F978-3-319-46466-4_14&token2=exp=1492181498~acl=%2Fstatic%2Fpdf%2F605%2Fchp%25253A10.1007%25252F978-3-319-46466-4_14.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fchapter%252F10.1007%252F978-3-319-46466-4_14*~hmac=b772943d8cd5f914e7bc84a30ddfdf0ef87991bee1d52717cb4930e3eccb0e63
FPNN: Field Probing Neural Networks for 3D Data
https://arxiv.org/pdf/1605.06240.pdf
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views
https://arxiv.org/pdf/1505.05641.pdf
Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
https://arxiv.org/pdf/1610.07584.pdf
SurfNet: Generating 3D shape surfaces using deep residual networks
https://arxiv.org/pdf/1703.04079.pdf
3D ShapeNets: A Deep Representation for Volumetric Shapes：
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction：
Learning to generate chairs with convolutional neural networks：
Category-specific object reconstruction from a single image：
Enriching Object Detection with 2D-3D Registration and Continuous Viewpoint Estimation：
Completing 3d object shape from one depth image：
ShapeNet: An Information-Rich 3D Model Repository：
3D reconstruction of synapses with deep learning based on EM Images：
Analysis and synthesis of 3d shape families via deep-learned generative models of surfaces：
FPNN: Field Probing Neural Networks for 3D Data：
Unsupervised Learning of 3D Structure from Images：
Deep learning 3d shape surfaces using geometry images：
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views：
Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling：
SurfNet: Generating 3D shape surfaces using deep residual networks：
推薦系統(tǒng)
經(jīng)典模型：
Autorec: Autoencoders meet collaborative filtering
http://users.cecs.anu.edu.au/~akmenon/papers/autorec/autorec-paper.pdf
User modeling with neural network for review rating prediction
https://www.google.com.hk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwj35dyVo6nTAhWEnpQKHSAwCw4QFggjMAA&url=http%3a%2f%2fwww%2eaaai%2eorg%2focs%2findex%2ephp%2fIJCAI%2fIJCAI15%2fpaper%2fdownload%2f11051%2f10849&usg=AFQjCNHeMJX8AZzoRF0ODcZE_mXazEktUQ
Collaborative Deep Learning for Recommender Systems
https://arxiv.org/pdf/1409.2944.pdf
A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp1159-songA.pdf
A neural probabilistic model for context based citation recommendation
http://www.personal.psu.edu/wzh112/publications/aaai_slides.pdf
Hybrid Recommender System based on Autoencoders
acm=1492356698_958d1b64105cd41b9719c8d285736396”>http://delivery.acm.org/10.1145/2990000/2988456/p11-strub.pdf?ip=123.138.79.12&id=2988456&acc=ACTIVE%20SERVICE&key=BF85BBA5741FDC6E%2EB37B3B2DF215A17D%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=751612499&CFTOKEN=37099060&acm=1492356698_958d1b64105cd41b9719c8d285736396
Wide & Deep Learning for Recommender Systems
https://arxiv.org/pdf/1606.07792.pdf
Deep Neural Networks for YouTube Recommendations
https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks
http://www.wanghao.in/paper/NIPS16_CRAE.pdf
Neural Collaborative Filtering
http://www.comp.nus.edu.sg/~xiangnan/papers/ncf.pdf
Recurrent Recommender Networks
http://alexbeutel.com/papers/rrn_wsdm2017.pdf
Autorec: Autoencoders meet collaborative filtering：
User modeling with neural network for review rating prediction：
A neural probabilistic model for context based citation recommendation：
A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems：
Collaborative Deep Learning for Recommender Systems：
Wide & Deep Learning for Recommender Systems：
Deep Neural Networks for YouTube Recommendations：
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks：
Neural Collaborative Filtering：
Recurrent Recommender Networks：
細(xì)粒度圖像分析
經(jīng)典模型：
Part-based R-CNNs for Fine-grained Category Detection
https://people.eecs.berkeley.edu/~nzhang/papers/eccv14_part.pdf
Bird Species Categorization Using Pose Normalized Deep Convolutional Nets
http://www.bmva.org/bmvc/2014/files/paper071.pdf
Mask-CNN: Localizing Parts and Selecting Descriptors for Fine-Grained Image Recognition
https://arxiv.org/pdf/1605.06878.pdf
The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification
http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Xiao_The_Application_of_2015_CVPR_paper.pdf
Bilinear CNN Models for Fine-grained Visual Recognition
http://vis-www.cs.umass.edu/bcnn/docs/bcnn_iccv15.pdf
Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval
https://arxiv.org/pdf/1604.04994.pdf
Near Duplicate Image Detection: min-Hash and tf-idf Weighting
https://www.robots.ox.ac.uk/~vgg/publications/papers/chum08a.pdf
Fine-grained image search
https://users.eecs.northwestern.edu/~jwa368/pdfs/deep_ranking.pdf
Efficient large-scale structured learning
http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Branson_Efficient_Large-Scale_Structured_2013_CVPR_paper.pdf
Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks
https://arxiv.org/pdf/1504.08289.pdf
Part-based R-CNNs for Fine-grained Category Detection：
Bird Species Categorization Using Pose Normalized Deep Convolutional Nets
Mask-CNN: Localizing Parts and Selecting Descriptors for Fine-Grained Image Recognition
The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification：
Bilinear CNN Models for Fine-grained Visual Recognition：
Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval：
Near Duplicate Image Detection: min-Hash and tf-idf Weighting：
Fine-grained image search：
Efficient large-scale structured learning：
Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks：
圖像壓縮
經(jīng)典模型：
Auto-Encoding Variational Bayes
https://arxiv.org/pdf/1312.6114.pdf
k-Sparse Autoencoders
https://arxiv.org/pdf/1312.5663.pdf
Contractive Auto-Encoders: Explicit Invariance During Feature Extraction
http://www.iro.umontreal.ca/~lisa/pointeurs/ICML2011_explicit_invariance.pdf
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
http://www.jmlr.org/papers/volume11/vincent10a/vincent10a.pdf
Tutorial on Variational Autoencoders
https://arxiv.org/pdf/1606.05908.pdf
End-to-end Optimized Image Compression
https://openreview.net/pdf?id=rJxdQ3jeg
Guetzli: Perceptually Guided JPEG Encoder
https://arxiv.org/pdf/1703.04421.pdf
Auto-Encoding Variational Bayes：
k-Sparse Autoencoders：
Contractive Auto-Encoders: Explicit Invariance During Feature Extraction：
Stacked Denoising Autoencoders: Learning Useful Representa-tions in a Deep Network with a Local Denoising Criterion：
Tutorial on Variational Autoencoders：
End-to-end Optimized Image Compression：
Guetzli: Perceptually Guided JPEG Encoder：
引用塊內(nèi)容
NLP領(lǐng)域
教程：http://cs224d.stanford.edu/syllabus.html
注：
1）目前接觸了該領(lǐng)域的一點皮毛，后續(xù)會慢慢更新。
2）也希望研究該領(lǐng)域的朋友們做出一些貢獻(xiàn)，期待你們的加入。
語音識別領(lǐng)域
注：
1）目前還沒有詳細(xì)了解語音識別領(lǐng)域，后續(xù)會慢添加更新。
2）也希望研究該領(lǐng)域的朋友們做出一些貢獻(xiàn)，期待你們的加入。
AGI – 通用人工智能領(lǐng)域
注：
1）目前還沒有詳細(xì)了解語音識別領(lǐng)域，后續(xù)會慢添加。
2）也希望研究該領(lǐng)域的朋友們做出一些貢獻(xiàn)，期待你們的加入。
深度學(xué)習(xí)引起的一些新的技術(shù)：
視頻教程：
https://cn.udacity.com/course/reinforcement-learning–ud600
注：由于還沒有學(xué)習(xí)到該部分，僅僅知道這個新的概念，后面會慢慢添加進(jìn)來。
Tutorial：http://icml.cc/2016/tutorials/deep_rl_tutorial.pdf
課程： http://rll.berkeley.edu/deeprlcourse/
DeepMind：
https://deepmind.com/blog/deep-reinforcement-learning/
終結(jié)語
注：
1. 好了，終于差不多啦，為了寫這個東西，花費了很多時間，但是通過這個總結(jié)以后，我也學(xué)到了很多，我真正的認(rèn)識到DeepLearning已經(jīng)貫穿了整個CV領(lǐng)域。如果你從事CV領(lǐng)域的話，我建議你花一些時間去了解深度學(xué)習(xí)吧！畢竟，它正在顛覆這個鄰域！
2. 由于經(jīng)驗有限，可能會有一些錯誤，希望大家多多包涵。如果你有任何問題，可以你消息給我，我會及時的回復(fù)大家。
3. 由于本博客是我自己原創(chuàng)，如需轉(zhuǎn)載，請聯(lián)系我。
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總結(jié)
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