Summarizing videos with Attention
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Slide for study session given by Christian Saravia at Arithmer inc. It is a summary of methods for video summarization using attention mechanism. Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。 Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
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http://imatge-upc.github.io/telecombcn-2016-dlcv/
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https://telecombcn-dl.github.io/2017-dlcv/
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https://github.com/telecombcn-dl/dlmm-2017-dcu
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https://telecombcn-dl.github.io/2017-dlcv/
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http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
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Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.Visualization of Deep Learning Models (D1L6 2017 UPC Deep Learning for Comput...
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https://telecombcn-dl.github.io/2017-dlcv/
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Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
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Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
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Arithmer Seminar is weekly held, where professionals from within our company give lectures on their respective expertise.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
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本スライドは、弊社のNLPソリューション・AIチャットボットについて、2021年7月20日にwebセミナーを行った際の資料になります。
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Arithmer R3 Introduction
本スライドは、弊社のR3チーム（3Dソリューション開発チーム）によりArithmer Seminarで使用されたものです。
弊社のArithmerR3ソリューションの基礎的な技術についてご理解いただけるよう制作した資料となります。
Arithmer Seminar is weekly held, where professionals from within our company give lectures on their respective expertise.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Arithmer Inspection Introduction
本スライドは、弊社のInspectionチーム（画像認識開発チーム）によりArithmer Seminarで使用されたものです。
弊社のArithmerInspectionソリューションの基礎的な技術についてご理解いただけるよう制作した資料となります。
Arithmer Seminar is weekly held, where professionals from within our company give lectures on their respective expertise.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
全力解説！Transformer
本スライドは、弊社の梅本により弊社内の技術勉強会で使用されたものです。
近年注目を集めるアーキテクチャーである「Transformer」の解説スライドとなっております。
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Arithmer NLP Introduction
本スライドは、弊社の鈴木により2021年3月25日のArithmer Seminarで使用されたものです。
弊社のNLPソリューションの基礎的な部分について、営業メンバー・非DBエンジニアに理解してもらう目的で作った資料になります。
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Introduction of Quantum Annealing and D-Wave Machines
This slide was used for Arithmer seminar in April 2021, by Dr. Yuki Bando.
It is for introduction of quantum computer, D-wave series, and its application to optimization problems in industry.
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Arithmer OCR Introduction
本スライドは、弊社の石橋により2021年1月14日のArithmer Seminarで使用されたものです。
弊社のOCRソリューションの基礎的な部分について、営業メンバー・非DBエンジニアに理解してもらう目的で作った資料になります。
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Arithmer Dynamics Introduction
本スライドは、弊社の上坂により2021年1月21日のArithmer Seminarで使用されたものです。
弊社のDynamicsソリューション（動画解析）の基礎的な部分について、営業メンバー・非Dynamicsエンジニアに理解してもらう目的で作った資料になります。
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
ArithmerDB Introduction
本スライドは、弊社の曽根により2021年1月28日のArithmer Seminarで使用されたものです。
弊社のDBソリューションの基礎的な部分について、営業メンバー・非DBエンジニアに理解してもらう目的で作った資料になります。
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
3D human body modeling from RGB images
Slide for study session given by Dr. Enrico Rinaldi at Arithmer inc.
It is a summary of established methods for parametric modeling of 3D human body "SMPL", which has many possible applications in apparel/health care industry.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Object Pose Estimation
Slide for study session given by Ryosuke Sasaki at Arithmer inc.
It is a summary of recent methods for object pose estimation in robotics using deep learning.
He entered Ph.D course at Univ. of Tokyo in April 2020.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Recommendation algorithm using reinforcement learning
Slide for study session given by Lu Juanjuan at Arithmer inc.
It is a summary of recent methods for recommendation system using reinforcement learning.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Survey on self supervised image segmentation
Slide for study session given by Yann Le Guilly at Arithmer inc.
It is a summary of recent methods for semantic segmentation using self-supervised learning.
It also contains links to his well-summarized notes.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
dataScienceofPhysics
Slide for Arithmer Seminar given by Dr. Enrico Rinaldi (RIKEN) at Arithmer inc.
The topic is how data science is used at frontier in research of physics, especially in high-energy and condensed matter physics.
Actually, three month after from this seminar, Dr. Rinaldi joined Arithmer as AI researcher/engineer!
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
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Summarizing videos with Attention
- 1. 1 2020/12/15 Summarizing Videos with Attention Arithmer Dynamics Div. Christian Saravia
- 2. Self-Introduction Christian Martin Saravia Hernandez ● Graduate School ○ Universidad Peruana de Ciencias Aplicadas ○ Bs. Software Engineering ● Former Job ○ Ficha Inc ■ Algorithm Development Engineer ● Research & application of deep learning in computer vision problems ● Current Job ○ Application of machine learning / deep learning to computer vision problems ■ Object detection ■ Object classification ○ Research topics ■ Attention & Transformers
- 3. Purpose Purpose of this material ● Explore a solution to the task of video summarization using attention.
- 4. Agenda Contents ● Introduction ○ Motivation ○ Contributions ● Dataset ● VASNet ○ Feature Extraction ○ Attention Network ○ Regressor Network ● Inference ○ Changepoint Detection ○ Kernel Temporal Segmentation ● Results ○ Measuring method ○ Dataset Results
- 5. Introduction Motivation ● Early video summarization methods were based on unsupervised methods, leveraging low level spatio-temporal features and dimensionality reduction with clustering techniques.Success of these methods solely stands on the ability to define distance/cost functions between the keyshots/frames with respect to the original video. ● Current state of the art methods for video summarization are based on recurrent encoder-decoder architectures, usually with bi- directional LSTM or GRU and soft attention. They are computationally demanding, especially in the bi-directional configuration.
- 6. Introduction Contribution 1. A novel approach to sequence to sequence transformation for video summarization based on soft, self-attention mechanism. In contrast, current state of the art relies on complex LSTM/GRU encoder-decoder methods. 1. A demonstration that a recurrent network can be successfully replaced with simpler, attention mechanism for the video summarization.
- 7. Dataset Dataset ● TVSum Dataset: https://github.com/yalesong/tvsum ● SumMe Dataset: https://gyglim.github.io/me/vsum/index.html
- 8. VASNet Feature Extraction ● Given a time interval t, every 15 frames are collected in an ordered set X ● Each set then is used as input to GoogLeNet for feature extraction ● Then we extract the Pool 5 layer of GoogLeNet, which is a 1024 dimensional array (D = 1024).
- 9. VASNet Attention Network ● Unnormalized self-attention weight et,i is calculated as an alignment between input feature Xt and the entire input sequence Where, N: Number of frames U, V: Network weight matrices estimated together with other parameters of the network during optimization s: Scale paramenter ● The attention weights αt are true probabilities representing the importance of input features with respect to the desired frame level score at the time t
- 11. VASNet Regressor Network ● Linear transformation C is then applied to each input and the results then weighted with attention vector αt and averaged. ● The output is a context vector ct which is used for the final frame score regression. ● The context vector ct is then projected by a single layer, fully connected network with linear activation and residual sum followed by dropout and layer normalization.
- 13. Inference Inference ● The output of the model VASNet is a probability of importance per frame ● This probability must be analyzed in the range of the scene it corresponds ● However to get the number of frames is relative per video ● The problem to find the frames where a change a scene exist is called changepoint detection. ● For the datasets used, the changepoints (cps) are already calculated by using KTS algorithm with hyperparameter tuning
- 14. Inference Changepoint detection ● In statistical analysis, change detection or change point detection tries to identify times when the probability distribution of a stochastic process or time series changes. In general the problem concerns both detecting whether or not a change has occurred, or whether several changes might have occurred, and identifying the times of any such changes.
- 15. Inference Kernel Temporal Segmentation (KTS) ● Kernel Temporal Segmentation (KTS) method splits the video into a set of non-intersecting temporal segments. ● It treats the cps detection as a dynamic programming problem. ● The method is fast and accurate when combined with high- dimensional descriptors.
- 16. Results Measuring method P: Precision R: Recall F Score: [2 * P * R / (P + R)] * 100
- 18. Results Dataset Results ● Long video: https://youtu.be/873CBVbPJVE ● Summarize: https://youtu.be/weW4memH3Dg ● Full playlist: https://www.youtube.com/playlist?list=PLEdpjt8KmmQMfQEat4HvuIxORwiO9q9DB
- 19. References Reference ● VASNet: https://arxiv.org/pdf/1812.01969.pdf ● VASNet official implementation: https://github.com/ok1zjf/VASNet ● KTS implementation: https://github.com/TatsuyaShirakawa/KTS ● Video summarization datasets and review: https://hal.inria.fr/hal-01022967/PDF/video_summarization.pdf ● Issue on testing on own videos: https://github.com/ok1zjf/VASNet/issues/2