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Third time lucky for the winner of AWS DeepRacer League in Chicago and new world records at re:MARS

The AWS DeepRacer League is the world’s first global autonomous racing league, open to anyone. Developers of all skill levels can compete in person at 22 AWS events globally, or online via the AWS DeepRacer console, for a chance to win an expense paid trip to re:Invent 2019, where they will race to win the Championship Cup 2019.

AWS Summit Chicago – winners

On May 30th, the AWS DeepRacer league visited the AWS Summit in Chicago, which was the 11th live race of the 2019 season. The top three there were as enthusiastic as ever and eager to put their models to the test on the track.

The Chicago race was extremely close to seeing all of the top three participants break the 10-second barrier. Scott from A Cloud Guru at the topped the board with 9.35 seconds, closely followed by RoboCalvin at 10.23 seconds and szecsei with 10.79 seconds.

Before Chicago, the winner Scott from A cloud guru had competed in the very first race in Santa Clara and was knocked from the top spot in the last hour of racing! There he ended up 4th, with a time of 11.75 seconds. He tried again in Atlanta, but couldn’t do better than 8th recording a time of 12.69 seconds. It was third time lucky for him in Chicago, where he was finally crowned champion and scored his winning ticket to the Championship Cup at re:Invent 2019!

Winners from Chicago RoboCalvin (2nd – 10.2 seconds), Scott (winner – 9.35 seconds), Szecsei (3rd – 10.7 seconds).

On to Amazon re:MARS, for lightning fast times and multiple world records!

On June 4th, the AWS DeepRacer League moved to the next race in Las Vegas, Nevada, where the inaugural re:MARS conference took place. Re:MARS is a new global AI event focused on Machine Learning, Automation, Robotics, and Space.

Over 2.5 days, AI enthusiasts visited the DeepRacer track to compete for the top prize. It was a competitive race; the world record was broken twice (the previous record was set in Seoul in April and was 7.998 seconds). John (who eventually came second), was first to break it and was in the lead with a time of 7.84 seconds for most of the afternoon before astronav (Anthony Navarro) knocked him off the top spot in the final few minutes of racing, with a winning time of 7.62 seconds. Competition was strong, and developers returned to the tracks multiple times after iterating on their model. Although the times were competitive, they were all cheering for each other and even sharing strategies. It was the fastest race we have seen yet – the top 10 were all under 10 seconds!

The winners from re:MARS John (2nd – 7.84 seconds), Anthony (1st – 7.62 seconds), Gustav (3rd – 8.23 seconds).

Developers of all skill levels can participate in the League

Participants in the league vary in their ability and experience in machine learning. Re:MARS, not surprisingly brought some speedy times, but developers there were still able to learn something new and build on their existing skills. Similarly, our winner from Chicago had some background in the field, but our 3rd place winner had absolutely none. The league is open to all and can help you reach your machine learning goals. The pre-trained models provided at the track make it possible for you to enter the league without building a model, or you can create your own from scratch in one of the workshops held at the event. And new this week is the racing tips page, providing developers with the most up to date tools to improve lap times, tips from AWS experts, and opportunities to connect with the DeepRacer community. Check it out today and start sharing your DeepRacer story!

Machine learning developers, with some or no experience before entering the league.

Another triple coming up!

The 2019 season is in the home stretch and during the week of June 10th, 3 more races are taking place. There will be a full round up on all the action next week, as we approach the last few chances on the summit circuit for developers to advance to the finals at re:Invent 2019. Start building today for your chance to win!

[P] Implementation of VoVNet(CVPRW’19)

Hi,

I implemented VoVNet which is efficient backbone network presented in CVPR workshop on CEFRL.

My implementations provide ImageNet classification and object detections in Detectron.

Highlight

  • 2x faster than DenseNet on ImageNet classification
  • more accurate than ResNet, especially on the small object detection

ImageNet classification : https://github.com/stigma0617/VoVNet.pytorch

Detectron : https://github.com/stigma0617/maskrcnn-benchmark-vovnet/tree/vovnet

submitted by /u/stigma0617
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Angle prediction problem [P]

Hello people, I would like to develop an algorithm to predict angles for my project. Now what I’m doing is collecting values from two gyro sensors which in the form of angles. One of these angles is the input and the other is to be predicted, the second sensor is used to check whether the predicted angle is right or wrong. Now what we have tried – – we checked the correlation here and it came out to be 91% which was expected and we checked that these angle values can be derived from a lookup table but these mappings change when derivative or rate of change of input changes. And this is my problem I’m not able to come up with an apt algo to solve this problem also the solution needs to less memory hungry😅. Which again a problem. We thought of fuzzy logic but again it is difficult to form proper membership functions. Please please people of Reddit help me!

submitted by /u/Shinigaami7
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[D] Text character visual similarity algo?

Hey, for an application I’m making I want to find the similarity between how two letters look.

For example, o and O have a high similarity but o and K do not.

Can someone guide me on what sort of techniques I would use (not necessarily ML, but this sounds like a DL task) in order to find a similarity between the look of two characters? It could be any character in any language that’s why I can’t just do it manually.

My proposed algorithm is as follows: 1. Accept 2 letters as argument 2. Generate same size image with same sized characters placed in them 3. Compute the similarity between the two images somehow 4. Get result

How would I do step 3? Any guidelines are appreciated. I’m currently looking into HOG classifiers, any other information is appreciated.

EDIT: My idea right now is to extract key features using algorithms like SURF, ORB etc (find the best one) and then compare 2 images them using cosine similarity between the two feature vectors. Would this work?

submitted by /u/bigDATAbig
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[D] Machine learning papers that look at the 2nd gradient /gradient of the gradient ?

I feel that looking at the 2nd gradient (gradient of the gradient (2nd derivative) ) may be interesting to look at but I can’t seem to query this. I haven’t found any papers on this.

But I feel that somebody must have looked into this. Has research on this never been performed? Or is there a specific phrase to query ?

submitted by /u/BatmantoshReturns
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[Discussion] Methods to use alternative form of reconstruction objective for VAE than pixelwise error

I am currently working on a project which involves improving the reconstruction capability of the VAE perceptually. Since the basic VAE objective uses the pixelwise error for the reconstruction part, the generated images have a peculiar blurry characteristic which makes them perceptually unreal. I did keyword searches on Scholar and ResearchGate, but was not able to find works that replace this pixelwise metric with something more appropriate for images.

The closest I got was with the paper titled “Autoencoding beyond pixels using a learned similarity metric” https://arxiv.org/pdf/1512.09300.pdf. This is a great piece of work and I find the idea of combining the GAN discriminator with the VAE superb.

In my search, I also found the flow based papers such as GLOW and RealNVP. But these use the reversible operations because of which, the posterior probability can be easily calculated since it is a deterministic function of the prior probability. I am actually looking for the variational inference generative models which simply use a different form of reconstruction objective for better perceptual results.

I kindly request all the fellow redditors to please provide me with works that you are aware of. It would be a great help. Thanking you.

Best regards,

akanimax

submitted by /u/akanimax
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Google at CVPR 2019

Andrew Helton, Editor, Google AI Communications

This week, Long Beach, CA hosts the 2019 Conference on Computer Vision and Pattern Recognition (CVPR 2019), the premier annual computer vision event comprising the main conference and several co-located workshops and tutorials. As a leader in computer vision research and a Platinum Sponsor, Google will have a strong presence at CVPR 2019—over 250 Googlers will be in attendance to present papers and invited talks at the conference, and to organize and participate in multiple workshops.

If you are attending CVPR this year, please stop by our booth and chat with our researchers who are actively pursuing the next generation of intelligent systems that utilize the latest machine learning techniques applied to various areas of machine perception. Our researchers will also be available to talk about and demo several recent efforts, including the technology behind predicting pedestrian motion, the Open Images V5 dataset and much more.

You can learn more about our research being presented at CVPR 2019 in the list below (Google affiliations highlighted in blue)

Area Chairs include:
Jonathan T. Barron, William T. Freeman, Ce Liu, Michael Ryoo, Noah Snavely

Oral Presentations
Relational Action Forecasting
Chen Sun, Abhinav Shrivastava, Carl Vondrick, Rahul Sukthankar, Kevin Murphy, Cordelia Schmid

Pushing the Boundaries of View Extrapolation With Multiplane Images
Pratul P. Srinivasan, Richard Tucker, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng, Noah Snavely

Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
Chenxi Liu, Liang-Chieh Chen, Florian Schroff, Hartwig Adam, Wei Hua, Alan L. Yuille, Li Fei-Fei

AutoAugment: Learning Augmentation Strategies From Data
Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, Quoc V. Le

DeepView: View Synthesis With Learned Gradient Descent
John Flynn, Michael Broxton, Paul Debevec, Matthew DuVall, Graham Fyffe, Ryan Overbeck, Noah Snavely, Richard Tucker

Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation
He Wang, Srinath Sridhar, Jingwei Huang, Julien Valentin, Shuran Song, Leonidas J. Guibas

Do Better ImageNet Models Transfer Better?
Simon Kornblith, Jonathon Shlens, Quoc V. Le

TextureNet: Consistent Local Parametrizations for Learning From High-Resolution Signals on Meshes
Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkhouser, Matthias Niessner, Leonidas J. Guibas

Diverse Generation for Multi-Agent Sports Games
Raymond A. Yeh, Alexander G. Schwing, Jonathan Huang, Kevin Murphy

Occupancy Networks: Learning 3D Reconstruction in Function Space
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, Andreas Geiger

A General and Adaptive Robust Loss Function
Jonathan T. Barron

Learning the Depths of Moving People by Watching Frozen People
Zhengqi Li, Tali Dekel, Forrester Cole, Richard Tucker, Noah Snavely, Ce Liu, William T. Freeman

Composing Text and Image for Image Retrieval – an Empirical Odyssey
Nam Vo, Lu Jiang, Chen Sun, Kevin Murphy, Li-Jia Li, Li Fei-Fei, James Hays

Learning to Synthesize Motion Blur
Tim Brooks, Jonathan T. Barron

Neural Rerendering in the Wild
Moustafa Meshry, Dan B. Goldman, Sameh Khamis, Hugues Hoppe, Rohit Pandey, Noah Snavely, Ricardo Martin-Brualla

Neural Illumination: Lighting Prediction for Indoor Environments
Shuran Song, Thomas Funkhouser

Unprocessing Images for Learned Raw Denoising
Tim Brooks, Ben Mildenhall, Tianfan Xue, Jiawen Chen, Dillon Sharlet, Jonathan T. Barron

Posters
Co-Occurrent Features in Semantic Segmentation
Hang Zhang, Han Zhang, Chenguang Wang, Junyuan Xie

CrDoCo: Pixel-Level Domain Transfer With Cross-Domain Consistency
Yun-Chun Chen, Yen-Yu Lin, Ming-Hsuan Yang, Jia-Bin Huang

Im2Pencil: Controllable Pencil Illustration From Photographs
Yijun Li, Chen Fang, Aaron Hertzmann, Eli Shechtman, Ming-Hsuan Yang

Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis
Qi Mao, Hsin-Ying Lee, Hung-Yu Tseng, Siwei Ma, Ming-Hsuan Yang

Revisiting Self-Supervised Visual Representation Learning
Alexander Kolesnikov, Xiaohua Zhai, Lucas Beyer

Scene Graph Generation With External Knowledge and Image Reconstruction
Jiuxiang Gu, Handong Zhao, Zhe Lin, Sheng Li, Jianfei Cai, Mingyang Ling

Scene Memory Transformer for Embodied Agents in Long-Horizon Tasks
Kuan Fang, Alexander Toshev, Li Fei-Fei, Silvio Savarese

Spatially Variant Linear Representation Models for Joint Filtering
Jinshan Pan, Jiangxin Dong, Jimmy S. Ren, Liang Lin, Jinhui Tang, Ming-Hsuan Yang

Target-Aware Deep Tracking
Xin Li, Chao Ma, Baoyuan Wu, Zhenyu He, Ming-Hsuan Yang

Temporal Cycle-Consistency Learning
Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, Andrew Zisserman

Depth-Aware Video Frame Interpolation
Wenbo Bao, Wei-Sheng Lai, Chao Ma, Xiaoyun Zhang, Zhiyong Gao, Ming-Hsuan Yang

MnasNet: Platform-Aware Neural Architecture Search for Mobile
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, Quoc V. Le

A Compact Embedding for Facial Expression Similarity
Raviteja Vemulapalli, Aseem Agarwala

Contrastive Adaptation Network for Unsupervised Domain Adaptation
Guoliang Kang, Lu Jiang, Yi Yang, Alexander G. Hauptmann

DeepLight: Learning Illumination for Unconstrained Mobile Mixed Reality
Chloe LeGendre, Wan-Chun Ma, Graham Fyffe, John Flynn, Laurent Charbonnel, Jay Busch, Paul Debevec

Detect-To-Retrieve: Efficient Regional Aggregation for Image Search
Marvin Teichmann, Andre Araujo, Menglong Zhu, Jack Sim

Fast Object Class Labelling via Speech
Michael Gygli, Vittorio Ferrari

Learning Independent Object Motion From Unlabelled Stereoscopic Videos
Zhe Cao, Abhishek Kar, Christian Hane, Jitendra Malik

Peeking Into the Future: Predicting Future Person Activities and Locations in Videos
Junwei Liang, Lu Jiang, Juan Carlos Niebles, Alexander G. Hauptmann, Li Fei-Fei

SpotTune: Transfer Learning Through Adaptive Fine-Tuning
Yunhui Guo, Honghui Shi, Abhishek Kumar, Kristen Grauman, Tajana Rosing, Rogerio Feris

NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
Golnaz Ghiasi, Tsung-Yi Lin, Quoc V. Le

Class-Balanced Loss Based on Effective Number of Samples
Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, Serge Belongie

FEELVOS: Fast End-To-End Embedding Learning for Video Object Segmentation
Paul Voigtlaender, Yuning Chai, Florian Schroff, Hartwig Adam, Bastian Leibe, Liang-Chieh Chen

Inserting Videos Into Videos
Donghoon Lee, Tomas Pfister, Ming-Hsuan Yang

Volumetric Capture of Humans With a Single RGBD Camera via Semi-Parametric Learning
Rohit Pandey, Anastasia Tkach, Shuoran Yang, Pavel Pidlypenskyi, Jonathan Taylor, Ricardo Martin-Brualla, Andrea Tagliasacchi, George Papandreou, Philip Davidson, Cem Keskin, Shahram Izadi, Sean Fanello

You Look Twice: GaterNet for Dynamic Filter Selection in CNNs
Zhourong Chen, Yang Li, Samy Bengio, Si Si

Interactive Full Image Segmentation by Considering All Regions Jointly
Eirikur Agustsson, Jasper R. R. Uijlings, Vittorio Ferrari

Large-Scale Interactive Object Segmentation With Human Annotators
Rodrigo Benenson, Stefan Popov, Vittorio Ferrari

Self-Supervised GANs via Auxiliary Rotation Loss
Ting Chen, Xiaohua Zhai, Marvin Ritter, Mario Lučić, Neil Houlsby

Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks
Stephen James, Paul Wohlhart, Mrinal Kalakrishnan, Dmitry Kalashnikov, Alex Irpan, Julian Ibarz, Sergey Levine, Raia Hadsell, Konstantinos Bousmalis

Using Unknown Occluders to Recover Hidden Scenes
Adam B. Yedidia, Manel Baradad, Christos Thrampoulidis, William T. Freeman, Gregory W. Wornell

Workshops
Computer Vision for Global Challenges
Organizers include: Timnit Gebru, Ernest Mwebaze, John Quinn

Deep Vision 2019
Invited speakers include: Pierre Sermanet, Chris Bregler

Landmark Recognition
Organizers include: Andre Araujo, Bingyi Cao, Jack Sim, Tobias Weyand

Image Matching: Local Features and Beyond
Organizers include: Eduard Trulls

3D-WiDGET: Deep GEneraTive Models for 3D Understanding
Invited speakers include: Julien Valentin

Fine-Grained Visual Categorization
Organizers include: Christine Kaeser-Chen
Advisory panel includes: Hartwig Adam

Low-Power Image Recognition Challenge (LPIRC)
Organizers include: Aakanksha Chowdhery, Achille Brighton, Alec Go, Andrew Howard, Bo Chen, Jaeyoun Kim, Jeff Gilbert

New Trends in Image Restoration and Enhancement Workshop and Associated Challenges
Program chairs include: Vivek Kwatra, Peyman Milanfar, Sebastian Nowozin, George Toderici, Ming-Hsuan Yang

Spatio-temporal Action Recognition (AVA) @ ActivityNet Challenge
Organizers include: David Ross, Sourish Chaudhuri, Radhika Marvin, Arkadiusz Stopczynski, Joseph Roth, Caroline Pantofaru, Chen Sun, Cordelia Schmid

Third Workshop on Computer Vision for AR/VR
Organizers include: Sofien Bouaziz, Serge Belongie

DAVIS Challenge on Video Object Segmentation
Organizers include: Jordi Pont-Tuset, Alberto Montes

Efficient Deep Learning for Computer Vision
Invited speakers include: Andrew Howard

Fairness Accountability Transparency and Ethics in Computer Vision
Organizers include: Timnit Gebru, Margaret Mitchell

Precognition Seeing through the Future
Organizers include: Utsav Prabhu

Workshop and Challenge on Learned Image Compression
Organizers include: George Toderici, Michele Covell, Johannes Ballé, Eirikur Agustsson, Nick Johnston

When Blockchain Meets Computer Vision & AI
Invited speakers include: Chris Bregler

Applications of Computer Vision and Pattern Recognition to Media Forensics
Organizers include: Paul Natsev, Christoph Bregler

Tutorials
Towards Relightable Volumetric Performance Capture of Humans
Organizers include: Sean Fanello, Christoph Rhemann, Graham Fyffe, Jonathan Taylor, Sofien Bouaziz, Paul Debevec, Shahram Izadi

Learning Representations via Graph-structured Networks
Organizers include: Ming-Hsuan Yang

[D] Rosalind Picard: Affective Computing | Artificial Intelligence Podcast

[D] Rosalind Picard: Affective Computing | Artificial Intelligence Podcast

Rosalind Picard is a professor at MIT, director of the Affective Computing Research Group at the MIT Media Lab, and co-founder of two companies, Affectiva and Empatica. Over two decades ago she launched the field of affective computing with her book of the same name. This book described the importance of emotion in artificial and natural intelligence, the vital role emotion communication has to relationships between people in general and in human-robot interaction. I really enjoyed talking with Roz over so many topics including emotion, ethics, privacy, wearable computing, her recent work in epilepsy, and even love and meaning.

Video: https://www.youtube.com/watch?v=kq0VO1FqE6I

https://i.redd.it/rkc34eetwx431.png

Outline:

0:00 – Introduction

1:00 – Affective computing

2:45 – Clippy

5:03 – Diversity in computer science

5:55 – Emotion in AI

8:40 – Privacy

18:10 – Forming a connection with AI systems

30:31 – Emotion

39:05 – Measuring signals from the brain and the body

50:20 – Future AI systems

53:50 – Faith and science

56:35 – Meaning of life

submitted by /u/UltraMarathonMan
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.