LookingFastandSlow: Memory-GuidedMobileVideoObjectDetection

Posted 2022-03-22 hugeng007

Google put the method to extract different feature
based on Slow Network and Fast Network

The First Colum	The Second Column
innovation point1	基于存储引导的交替模型
InterIeaved Models	slow network and fastnetwork is made up by two MobilNetV2 the depth multiplier of the two models are different, before is 1.4,and the after is 0.35
innovation point2	记忆单元, Memory module 存储模型， LSTM可以高效处理时序信息 但是卷积运算量大 ConvLSTM将CNN与LSTM结合
ConLSTM is designed by the 时序时间信息的图像
1 innovation of the ConvLSTM	增加了bottleneck Gate 和output 的跳跃连接
2 innovation of the ConvLSTM	将LSTM单元进行分组卷积 feature maps 原本是H * W * N 将其分为G group 每个LSTM处理的HWN/G 的feature maps
the step of LSTM	the first step : f(t) = sigmoid(W(f) * [h(t-1), x(t)] + b(f) ) LSTM include the activate function (sigmoid) and the action (pointwise) the first of the LSTM is sigmoid
The step of LSTM	The second step : i(t) = sigmoid( W(i) * [ h(t-1), x(t)] + b(i) ); C~(t) = tanh( W(C)* [h(t-1), x(t)] + b(c) ) Tanh create a new 候选值vector
The step of LSTM	The third step : C(t) = f(t) * C(t-1) + i(t) * C~(t)