pcjefflin.blogspot.com

AMMAI paper review

Sequence to Sequence – Video to Text. This paper focused on generating text from video. The main idea is to feed CNN feature and optical flow feature into a sequence LSTM to generate video caption sentence. For the combination of the CNN feature and optical flow. The word vectors use one-hot encoding. Microsoft Video Description corpus (MSVD). MPII Movie Description Corpus (MPII-MD). Montreal Video Annotation Dataset (M-VAD). DNN outperform GMM by large margin on variety of speech recognition benchmark.

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AMMAI paper review | pcjefflin.blogspot.com Reviews

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Sequence to Sequence – Video to Text. This paper focused on generating text from video. The main idea is to feed CNN feature and optical flow feature into a sequence LSTM to generate video caption sentence. For the combination of the CNN feature and optical flow. The word vectors use one-hot encoding. Microsoft Video Description corpus (MSVD). MPII Movie Description Corpus (MPII-MD). Montreal Video Annotation Dataset (M-VAD). DNN outperform GMM by large margin on variety of speech recognition benchmark.

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1 ammai paper review

2 introduction

3 architecture

4 the s2vt model

5 lstm detail part

6 dataset

7 experiment

8 for msvd

9 mpii md

10 m vad

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ammai paper review,introduction,architecture,the s2vt model,lstm detail part,dataset,experiment,for msvd,mpii md,m vad,example,張貼者：,lin jeff,沒有留言,以電子郵件傳送這篇文章,blogthis！,分享至 twitter,分享至 facebook,分享到 pinterest,experiments,conclusion,key modules,experiement

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AMMAI paper review | pcjefflin.blogspot.com Reviews

https://pcjefflin.blogspot.com

Sequence to Sequence – Video to Text. This paper focused on generating text from video. The main idea is to feed CNN feature and optical flow feature into a sequence LSTM to generate video caption sentence. For the combination of the CNN feature and optical flow. The word vectors use one-hot encoding. Microsoft Video Description corpus (MSVD). MPII Movie Description Corpus (MPII-MD). Montreal Video Annotation Dataset (M-VAD). DNN outperform GMM by large margin on variety of speech recognition benchmark.

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pcjefflin.blogspot.com

1

AMMAI paper review: Faster RCNN

http://pcjefflin.blogspot.com/2016/05/faster-rcnn.html

This paper focused on improving the speed and performance of fast-rcnn. Object proposal is the main bottleneck in fast rcnn architecture. Traditional object proposal methods work on pixel-wise or region-wise. This paper propose a new object proposal method(RPN layer) which works on conv5 layer which contains semantic meaning. RPN layer operates much faster than traditional method like Selective Search, EdgeBoxes., and come up with better bounding boxes. Faster-RCNN: 300 proposal prediction, 0.2 sec!

2

AMMAI paper review: DeepFace: Closing the Gap to Human-Level Performance in Face Verification

http://pcjefflin.blogspot.com/2016/05/deepface-closing-gap-to-human-level.html

DeepFace: Closing the Gap to Human-Level Performance in Face Verification. The modern face recognition method, the whole can be divided into multiple stage. 1 detection 2. alignment 3. representation 4. classification. This paper use deep learning skill on alignment and representation, and archive 97.35% on LFW datasets. 1 DNN architecture and learning method that leverage do well on generating face representations. 2 A effective facial alignment system based on explicit 3D modeling of faces.

3

AMMAI paper review: 五月 2016

http://pcjefflin.blogspot.com/2016_05_01_archive.html

Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. HMM is the common model to deal with the temporal variability of speech and GMMs is used to measure the acoustic input.(which is GMM-HMM system). This paper replaced the GMM with DNN and GMM-HMM turns into DNN-HMM system(which is called DBM-DNN in this paper). This paper shows the overview of the huge success of DNN on acoustic modeling. Text Understanding from Scratch. M is the size of sentence.

4

AMMAI paper review: 六月 2016

http://pcjefflin.blogspot.com/2016_06_01_archive.html

Sequence to Sequence – Video to Text. This paper focused on generating text from video. The main idea is to feed CNN feature and optical flow feature into a sequence LSTM to generate video caption sentence. For the combination of the CNN feature and optical flow. The word vectors use one-hot encoding. Microsoft Video Description corpus (MSVD). MPII Movie Description Corpus (MPII-MD). Montreal Video Annotation Dataset (M-VAD). 訂閱：文章 (Atom). Sequence to Sequence – Video to Text. 簡單主題技術提供： Blogger.

5

AMMAI paper review: Two-Stream Convolutional Networks for Action Recognition in Videos

http://pcjefflin.blogspot.com/2016/04/two-stream-convolutional-networks-for.html

Two-Stream Convolutional Networks for Action Recognition in Videos. This try convolution network on action recognition in videos. Before this paper, hand-crafted representations dominated in the scope of video. There are three main contribution in this work. 1 two-stream ConvNet architecture which incorporates spatial and temporal networks. 2 A ConvNet trained on multi-frame dense optical flow works well. 3 New multitask learning method applying to two different action classification datasets. This paper...

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pcjefflin.blogspot.com

AMMAI paper review

Sequence to Sequence – Video to Text. This paper focused on generating text from video. The main idea is to feed CNN feature and optical flow feature into a sequence LSTM to generate video caption sentence. For the combination of the CNN feature and optical flow. The word vectors use one-hot encoding. Microsoft Video Description corpus (MSVD). MPII Movie Description Corpus (MPII-MD). Montreal Video Annotation Dataset (M-VAD). DNN outperform GMM by large margin on variety of speech recognition benchmark.

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