上午有AY关于"计算机视觉和深度学习综述性工作介绍"的talk，简单记录如下，有时间补充。
总体感受 ：公司里比在学校小实验室瞎搞学得多得多
主题：Computational Cognition, Vision and learning & deep nets
印象最深的 The size of the dataset depends on the complexity of the task, the variability of the images and the classes.
一些概念记下来，需要后期补充：
1. Gauss 1800
2. conceptual model
3. fully conv deep net (Deeplab)
4. fully / weakly supervised
一些领域的介绍：
1. Occulusion detection
概念记录：
(1) occulusion boundary detect & bordeer-ownship
(2) 数据：Pascal
2. Human pose detection & semantic segmentation
(1) fully-connected CRF (conditional random fields)
3. Detecting symmetry axes
(1) Wei Shen st al., 2018
4. Siamese-Triplet Net: to learn similarity
(1) Tai Sing Lee collaboration, ICLR 2017
(2) 用于transfer learning
5. Text-captioning by m-RNN/LSTM
(1) Junhua Mao, ICLR 2015
(2) 介绍：
a. 从图中自动生成语言描述；
b. Text: m-RNN / image:CNN;
c. 结构大概是:
embedding 1-> embedding 2 -> RNN-> multimodel(这里有引入CNN）-> Softmax
(3) 会记住一些经常一起出现的词，举例：长颈鹿和树
(4) 现在这个task还是很难： 数据获取难/目前只在部分数据集上做成，迁移到别的数据集就不行
6. FELIX project：CT cancer detect
医疗数据，数据集一般都比较小
(1) multi-organ segmentation
(2) Dice similarity coefficient
(3) PDAL detection/segmentation
transfer learning
背景： 没有足够的annotated training data -> transfer learning
三个步骤：
1. 找closed problem B with enough training data
2. deep net on problem B
3. fine tune for A
举个例子：医疗上pain assessment from faces， 借用 face recognition
Feng Wang , ICIP 2017
一些记录：
(1) make this as a regression task
(2)CNN-> dense layer -> regression loss/ center loss 这两个loss分别代表什么？
(3)avoid overfitting: dense layer 神经元少一点
few shot learning
(1) key idea: relate the activations to the weights
(2) learn the mapping
这篇是乔思远的论文，本科同学，赞叹大神。
- deep net and random forest