ICCV2015+ICCV2017 active learning papers
标签： Paper ICCV
ICCV 2017
1. Learning Policies for Adaptive Tracking with Deep Feature Cascades PDF
cmu
使用deep的method效果会好，但速度变慢；而直接用相关滤波的方法，效果没那么好，但速度快。因此作者结合了两种方法，转化为决策问题，提出了一个可以自适应的方法 Early-Stopping Tracker (EAST)，容易track的frame就采用相关滤波即可，而难追踪的frame就继续进行convolution，得到表现好的deep feature。
使用Reinforce Learning训练一个agent，能够在每一层判断是否停止正向传播
将CNN中的future map导入相关滤波
2. Personalized Image Aesthetics pdf
Rutgers University
回归问题，预测用户的美学评分
通用评分预测+个人（内容+属性）网络
通用：cnn euclidean loss
个人：svr
分数及美学属性：cnn fine-tuning
内容属性：分类网络+k-means得到类别 之后fine-tune+soft-max
喂入用户评分与标准分相差较大的样本
3. Hard-Aware Deeply Cascaded Embedding pdf
pku
将网络分为多级，在每一级输出后选择loss较大的样本进入下一级进行训练
4. Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively
Combining Object Detectors pdf
nju
与1类似，关于object detection
5.Active Learning for Human Pose Estimation pdf
提出了multiple peak entropy， 用于measure uncertainty，
ICCV 2015
1. Context Aware Active Learning of Activity Recognition Model pdf
ucr
Activity Recognition，使用active learning 获得annotation
contribution：基于video中时空的联系，选择最informative的一个activity请求标注
使用CRF
2. Introducing Geometry in Active Learning for Image Segmentation pdf
a novel uncertainty function that combines traditional Feature Uncertainty with Geometric Uncertainty
3. Multi-class Multi-annotator Active Learning with Robust Gaussian Process for
Visual Recognition pdf
multi-class active learning
the problem of active learning with multiple annotators under the condition that multiple annotators may provide noisy labels has not been fully explored
与【1】类似，也是使用reinforce learning的方法选择informative的样本
4.Active Transfer Learning with Zero-Shot Priors:Reusing Past Datasets for Future Tasks pdf
image classification问题
选择svm临界部分数据
Fine-tuning
Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition pdf
- 数据量较小，而网络很复杂（参数多，维数高），不足以支撑从0开始训练