Author: Zongwei Zhou | 周縱葦
Weibo: @MrGiovanni
Email: zongweiz@asu.edu
CVPR官網(wǎng)信息：
CVPR錄用論文集
CVPR的流程
CVPR Workshop的流程
想合影的人列表...
- Fei-Fei Li
- Jia Li
- Kai-ming He
- Xiu-Shen Wei
- Hu-chuan Lu
- Pei-hua Li
- Yi Sun
- Hao Su
- Pheng-Ann Heng
- Lu Le
網(wǎng)上很有用的資源
[1] CVPR-2017-Abstracts-Collection
[2] CVPR 2017 論文解讀集錦
我的發(fā)表情況
論文：Fine-tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally
博客：Active Learning: 一個降低深度學習時間，空間，經(jīng)濟成本的解決方案
海報：Fine-tuning Convolutional Neural Networks for Biomedical Image Analysis:
Actively and Incrementally
CVPR2017有多牛
- 2620 valid submissions
- 783 papers
- 215 long and short orals
- 3 parallel tracks
- 127 sponsors
- 859k sponsorship fundings
- 4950 registrations
關(guān)于這一堆頂級論文，我按照他們展示的日期順序或者按照topic挑出一些我想要深入和作者交流的論文，策略是不求遍地開花，只求真正弄懂幾篇和我的興趣相關(guān)的論文即可。
Saturday, July 22
1- Deep Joint Rain Detection and Removal From a Single Image
相關(guān)：深度去雨--Deep Joint Rain Detection and Removal from a Single Image
除此之外，劉家瑛教授還介紹了她的「去雨」研究（Deep Joint Rain Detection and Removal from a Single Image）——基于多任務(wù)學習的方法對圖像中的「雨線」和「雨霧」進行檢測和去除，從而使圖像的主題內(nèi)容呈現(xiàn)的更加清晰。這項研究有著重要的實際意義，可應(yīng)用于惡劣天氣情況下的道路監(jiān)控以及自動駕駛等領(lǐng)域。[學術(shù)盛宴：微軟亞洲研究院CVPR 2017論文分享會全情回顧]
Sat, July 22, Afternoon, 1500–1700, Kamehameha I
備注：我個人覺得挺有意思的工作，可以用到ultrasound image的artificial噪音問題上！
根據(jù)作者的說法，Ground Truth實質(zhì)上是模擬出來的，然后在實際的有雨的照片上面測試，具體怎么衡量好壞，居然是用眼睛看... 額，那還怎么玩。不針對這篇論文，而是去雨這個研究領(lǐng)域，我個人感覺問題有很多欠解決，倒也不是說算法，而是這個問題的定義，怎么能這樣事兒的？
Thought: 看到很多different domain的問題，我想試試的是Quality Assessment在這上面。Domain Adaptation這個詞好像經(jīng)常一起出現(xiàn)，我以前從來沒有接觸過，感覺和Transfer Learning有點關(guān)系，對于Transfer Learning，我有很大的興趣。
Correlational Gaussian Processes for Cross-Domain Visual Recognition
Chengjiang Long, Gang Hua
[pdf] [bibtex]
Joint Geometrical and Statistical Alignment for Visual Domain Adaptation
Jing Zhang, Wanqing Li, Philip Ogunbona
[pdf] [slides] [bibtex]
Deep Transfer Network: Unsupervised Domain Adaptation
Xu Zhang, Felix Xinnan Yu, Shih-Fu Chang, Shengjin Wang
筆記：Deep transfer network: unsupervised domain adaptation
Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation
Hongliang Yan, Yukang Ding, Peihua Li, Qilong Wang, Yong Xu, Wangmeng Zuo
[pdf] [slides] [bibtex]
Unsupervised Pixel-Level Domain Adaptation With Generative Adversarial Networks
Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, Dilip Krishnan
[pdf] [Supp] [slides] [bibtex]
Learning an Invariant Hilbert Space for Domain Adaptation
Samitha Herath, Mehrtash Harandi, Fatih Porikli
[pdf] [Supp] [slides] [bibtex]
Domain Adaptation by Mixture of Alignments of Second- or Higher-Order Scatter Tensors
Piotr Koniusz, Yusuf Tas, Fatih Porikli
[pdf] [bibtex]
Deep Hashing Network for Unsupervised Domain Adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, Sethuraman Panchanathan
[pdf] [Supp] [slides] [bibtex]
A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation
Chunpeng Wu, Wei Wen, Tariq Afzal, Yongmei Zhang, Yiran Chen, Hai (Helen) Li
[pdf] [slides] [bibtex]
Adversarial Discriminative Domain Adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, Trevor Darrell
[pdf] [slides] [bibtex]
【深度學習】論文導讀：無監(jiān)督域適應(yīng)（Deep Transfer Network: Unsupervised Domain Adaptation）
Transfer learning and
domain adaptation
Lower layers: more general features. Transfer very well to other tasks.
Higher layers: more task specific.
Y Ganin and V Lempitsky, Unsupervised Domain Adaptation by Backpropagation, ICML 2015
Thought: Multi-Task 共用一個頭，支出很多尾巴，這樣就不用為同一個數(shù)據(jù)集訓練多個網(wǎng)絡(luò)了。
Weakly Supervised Actor-Action Segmentation via Robust Multi-Task Ranking
Yan Yan, Chenliang Xu, Dawen Cai, Jason J. Corso
[pdf] [bibtex]
Fully-Adaptive Feature Sharing in Multi-Task Networks With Applications in Person Attribute Classification
Yongxi Lu, Abhishek Kumar, Shuangfei Zhai, Yu Cheng, Tara Javidi, Rogerio Feris
[pdf] [slides] [bibtex]
Deep Multitask Architecture for Integrated 2D and 3D Human Sensing
Alin-Ionut Popa, Mihai Zanfir, Cristian Sminchisescu
[pdf] [slides] [bibtex]
Thought: 熱力圖來輔助定位ROI
這個事情有很多研究者都曾和我提到過，即用一個分類的ground truth來訓練一個網(wǎng)絡(luò)，然后通過分析后面幾層的熱力圖來輔助分割或者檢測。根據(jù)他們的可視化，的確靠譜，我感覺它背后的理論支撐應(yīng)該和multi-task一個道理。
Thought: 關(guān)于label的問題，腫瘤和非腫瘤，狗和非狗，benign，malignant，其他，實驗設(shè)計還是蠻簡單的，二分類器（貓和狗），三分類器（貓和狗和其他），然后分析兩個分類器對于貓／狗的分類效果。不過我更愿意用理論來解釋這個問題，實驗的話可能說服力不夠。
2- Borrowing Treasures From the Wealthy: Deep Transfer Learning Through Selective Joint Fine-Tuning
Sat, July 22, Morning, 0904, Kamehameha III
Thought: 喜歡這篇是因為最近我對于Fine-tune這個方法有一些疑惑，希望可以從作者的工作中找到解答。Fine-tune到底對于一個和ImageNet有很大差異的數(shù)據(jù)集，有多大的幫助，或者怎么樣Fine-tune可以把遷移學習這個方法用的更好？
3- On Compressing Deep Models by Low Rank and Sparse Decomposition
Sat, July 22, Morning, 0928, Kamehameha III
備注：壓縮存儲永遠是一個對我來說比較難的課題，這個技術(shù)在3D CNN上能起到很重要的作用?？赡軐τ诶碚摰囊髸容^高，還有編程量。
4- Unsupervised Pixel-Level Domain Adaptation With Generative Adversarial Networks.
Sat, July 22, Morning, 1001, Kamehameha III
Thought: 用GAN來做類似遷移學習的事情，找相似的domain，然后可以直接用feature extractor。
5- From Red Wine to Red Tomato: Composition With Context.
Sat, July 22, Afternoon, 1417, Kamehameha III
備注：題目好有意思
6- Fully-Adaptive Feature Sharing in Multi-Task Networks With Applications in Person Attribute Classification.
Sat, July 22, Afternoon, 1354, Kamehameha III
備注：感覺是一個逐步生長的網(wǎng)絡(luò)結(jié)構(gòu)（Jae吃飯的時候說的那個），abstract寫的很到位。
Q: RGB-D image: what's that?
A RGB-D image is simply a combination of a RGB image and its corresponding depth image. A depth image is an image channel in which each pixel relates to a distance between the image plane and the corresponding object in the RGB image.
[What is the difference between depth and RGB-depth images?](https://www.researchgate.net/post/What_is_the_difference_between_depth_and_RGB-depth_images [accessed Jul 21, 2017)
7- Diversified Texture Synthesis With Feed-Forward Networks
Sat, July 22, Morning, 0916, Kalākaua Ballroom C
8- Superpixel-Based Tracking-By-Segmentation Using Markov Chains
Sat, July 22, Morning, 1030–1230, Kamehameha I
9- Boundary-Aware Instance Segmentation
Sat, July 22, Morning, 1030–1230, Kamehameha I
10- Model-Based Iterative Restoration for Binary Document Image Compression With Dictionary Learning
Sat, July 22, Morning, 1030–1230, Kamehameha I
11- Learning by Association — A Versatile Semi-Supervised Training Method for Neural Networks
Sat, July 22, Morning, 1030–1230, Kamehameha I
12- DilatedResidualNetworks
Sat, July 22, Morning, 1030–1230, Kamehameha I
13- Split-BrainAutoencoders:UnsupervisedLearningby Cross-Channel Prediction
Sat, July 22, Morning, 1030–1230, Kamehameha I
14- The Incremental Multiresolution Matrix Factorization Algorithm
Sat, July 22, Morning, 1030–1230, Kamehameha I
15- Teaching Compositionality to CNNs
Sat, July 22, Morning, 1030–1230, Kamehameha I
16- Using Ranking-CNN for Age Estimation
Sat, July 22, Morning, 1030–1230, Kamehameha I
17- Accurate Single Stage Detector Using Recurrent Rolling Convolution
Sat, July 22, Morning, 1030–1230, Kamehameha I
18- A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation
Sat, July 22, Morning, 1030–1230, Kamehameha I
19- The Impact of Typicality for Informative Representative Selection
Sat, July 22, Morning, 1030–1230, Kamehameha I
20- Infinite Variational Autoencoder for Semi-Supervised Learning
Sat, July 22, Morning, 1030–1230, Kamehameha I
21- VariationalBayesianMultipleInstanceLearningWith Gaussian Processes
Sat, July 22, Morning, 1030–1230, Kamehameha I
22- Non-UniformSubsetSelectionforActiveLearningin Structured Data
Sat, July 22, Morning, 1030–1230, Kamehameha I
23- Pixelwise Instance Segmentation With a Dynamically Instantiated Network
Sat, July 22, Morning, 1030–1230, Kamehameha I
24- Object Detection in Videos With Tubelet Proposal Networks
Sat, July 22, Morning, 1030–1230, Kamehameha I
25- Feature Pyramid Networks for Object Detection
Sat, July 22, Morning, 1030–1230, Kamehameha I
26- Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation
Sat, July 22, Morning, 1030–1230, Kamehameha I
27- Fine-Grained Recognition of Thousands of Object Categories With Single-Example Training
Sat, July 22, Morning, 1030–1230, Kamehameha I
28- Improving Interpretability of Deep Neural Networks With Semantic Information
Sat, July 22, Morning, 1030–1230, Kamehameha I
29- Fast Boosting Based Detection Using Scale Invariant Multimodal Multiresolution Filtered Features
Sat, July 22, Morning, 1030–1230, Kamehameha I
30- Temporal Convolutional Networks for Action Segmentation and Detection
Sat, July 22, Morning, 1030–1230, Kamehameha I
31- Weakly Supervised Actor-Action Segmentation via Robust Multi-Task Ranking
Sat, July 22, Morning, 1030–1230, Kamehameha I
32- Crossing Nets: Combining GANs and VAEs With a Shared Latent Space for Hand Pose Estimation
Sat, July 22, Afternoon, 1330, Kamehameha III
33- Finding Tiny Faces
Sat, July 22, Afternoon, 1500–1700, Kamehameha I
34- Simple Does It: Weakly Supervised Instance and Semantic Segmentation
Sat, July 22, Afternoon, 1500–1700, Kamehameha I
35- Anti-Glare: Tightly Constrained Optimization for Eyeglass Reflection Removal
Sat, July 22, Afternoon, 1500–1700, Kamehameha I
36- Deep Joint Rain Detection and Removal From a Single Image
Sat, July 22, Afternoon, 1500–1700, Kamehameha I
37- Removing Rain From Single Images via a Deep Detail Network
Sat, July 22, Afternoon, 1500–1700, Kamehameha I
38- Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network
Sat, July 22, Afternoon, 1500–1700, Kamehameha I
39- Xception: Deep Learning With Depthwise Separable Convolutions
Sat, July 22, Afternoon, 1500–1700, Kamehameha I
41- Improving Pairwise Ranking for Multi-Label Image Classification
42- Stacked Generative Adversarial Networks
43- MoreIsLess:AMoreComplicatedNetworkWithLess Inference Complexity
44- CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
45- Learning Spatial Regularization With Image-Level Supervisions for Multi-Label Image Classification
46- Predictive-Corrective Networks for Action Detection
47- Unified Embedding and Metric Learning for Zero-Exemplar Event Detection
48- Query-Focused Video Summarization: Dataset
Sunday, July 23
1- Zero-Shot Learning - the Good, the Bad and the Ugly
Sun, July 23, Morning, 1000–1200, Kamehameha I
Q: Zero-Shot: what's that?
2- Densely Connected Convolutional Networks
**Note: **和resnet比較的時候有沒有花精力去fine resnet，還是一次到位作為baseline？下載代碼，以后的論文里面肯定要用到。
3- Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach
Thought: 濕狗的那個工作可以用到Quality Assessment上面，在ImageNet上面訓練一個模糊感知器，然后用到Colonoscopy上面。
4- Inverse Compositional Spatial Transformer Networks
Monday, July 24
1- Global optimalities in neural network training.
Thought: 關(guān)于Cross Validation, 我想做一個學習框架來去除cv，因為深度學習里面做cv很麻煩。
Evaluate the Performance Of Deep Learning Models in Keras
Preventing “Overfitting” of Cross-Validation data
We mostly have large datasets when it is not worth the trouble to do something like k-fold cross-validation. We just use a train/valid/test split. Cross-validation becomes useful when the dataset is tiny (like hundreds of examples), but then you can't typically learn a complex model. [Is cross-validation heavily used in deep learning or is it too expensive to be used?]
AFAIK, in deep learning you would normally tempt to avoid cross-validation because of the cost associated with training K different models. Instead of doing cross validation, you use a random subset of your training data as a hold-out for validation purposes.
For example, Keras deep learning library (which runs on top of theano or tensor flow), allows you to pass one of two parameters for the fit function (that performs training).
validation_split: what percentage of your training data should be held out for validation.
validation_data: a tuple of (X, y) to be used for validation. This parameter overrides the validation_split parameter value. [Is cross-validation heavily used in deep learning or is it too expensive to be used?]
Salient topic:
1- Instance-Level Salient Object Segmentation.
2- Deep Level Sets for Salient Object Detection.
3- Deeply Supervised Salient Object Detection With Short Connections.
4- What Is and What Is Not a Salient Object? Learning Salient Object Detector by Ensembling Linear Exemplar Regressors.
5- Learning to Detect Salient Objects With Image-Level Supervision.
6- Non-Local Deep Features for Salient Object Detection.
Ultrasound Image Artificial Issue:
1- Deep Multi-Scale Convolutional Neural Network for Dynamic Scene Deblurring.
備注：問題是他們用了監(jiān)督學習，有blur圖像和與之相對應(yīng)的clear圖像.
2- A Novel Tensor-Based Video Rain Streaks Removal Approach via Utilizing Discriminatively Intrinsic Priors.
3 -Deep Joint Rain Detection and Removal From a Single Image
4- Deep Video Deblurring for Hand-Held Cameras
Sat, July 22, Morning, 0904, Kalākaua Ballroom C
GAN
1- Unsupervised Pixel-Level Domain Adaptation With Generative Adversarial Networks
Sat, July 22, Morning, 1001, Kamehameha III
2- Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Sat, July 22, Morning, 1015, Kamehameha III
技術(shù)改動
1- FC4: Fully Convolutional Color Constancy With Confidence-Weighted Pooling
Sat, July 22, Morning, 1015, Kalākaua Ballroom C
