2017年解锁深度视频理解的潜力.pdf

1、Unlocking the Potential of Deep Video Understanding gPrincipal Research Manager,Microsoft Research视觉智能和深度学习简介 深度图像理解技术 深度视频理解技术 实际应用及市场化 未来技术趋势探讨AI Golden Age 黄金时代Big Data Image database organized according to WordNet hierarchy 100K+concepts/nodes 500+images/node on average tens of millions of human

2、-annotated images A Knowledge Ontology有标注的最大图像数据库AI Golden Age黄金时代Big Data“Deep”LearningBig Compute Architecture advancements 架构演化 Fully connected neural networks-FNN Convolutional neural networks-CNN Recurrent Neural Networks-RNN Long Short-Term Memory-LSTM Fully convolutional networks-FCN Deep res

3、idual networks Resnet Deep Learning Going“Deeper”Deep Learning Going“Deeper”深度学习深度学习“深入化深入化 Fully Connected Neural Networks FNN 全连神经网络8InputOutputNeural Networks:Approximate a function to map known input to known output Learn weights through trainingLimitations of FNN 缺点:Many parameters/weights（参数多）

4、High computation Potentially suffer severe overfitting（过拟合）Need large#of labeled dataConvolutional Neural Networks(CNN)(LeCun89)卷积神经网络9 shared-weight 参数共享参数共享 locally connected “locality”保留位置信息保留位置信息 Hierarchical view 多分辨率多分辨率Learned Low-Level Filters 学到的低层筛选器11学到的高层筛选器RNN/LSTM 递归神经网络 Recurrent Neur

5、al Networks The networks with loops in them allowing information to persist Model time-sequence data LSTM network Long Short-Term Memory Capable of learning long-range dependencies Model temporal dynamics welltanhtanhInput GateOutput GateCellForget GateOutputInputMemory cell remembers info that occu

6、rred at many timesteps in the past.Architecture advancements架构演化 Fully connected neural networks-FNN Convolutional neural networks-CNN Recurrent Neural Networks-RNN Long Short-Term Memory-LSTM Fully convolutional networks-FCN Deep residual networks Resnet Deep Learning Going“Deeper”Deep Learning Goi