Bob has a new blog: https://highnoongmt.wordpress.com/, he even advertized a studentship : PhD Studentship in Intelligent Machine Music Listening
Afonso
Dustin
Dirk
Gautam
Charles:
Tomasz
Greg Charvat sent me the following some time ago:
He also mentioned a a Job opportunity at hyperfine-research (Deep Learning Scientist)I setup a twitter feed for the IEEE International Symposium on Phased Array Systems and Technology
and thought you might find some of the tweets interesting:
https://twitter.com/Array2016
Most of the tweets will be on phased array projects people have completed, then we will have the occasional CFP tweet. So thought you might want to watch the feed for content for your blog.
This is the largest IEEE symposium in the northeast. Held every 3 years or so.
I am advocating a computational array session. Any thoughts or ideas on what that might look like?
Cheers,
Greg
Of interest: DeepMind's Nature Paper and Earlier Related Work by Jürgen Schmidhuber, 26 February 2015
Vladimir Koifman mentions that Imaging Remains Major Power Consumer in Wearables and while we are on the power consumption bit:
Figure from From: Reversible Circuits: Recent Accomplishments and Future Challenges
for an Emerging Technology? Rolf Drechsler and Robert WilleOn my twitter, Andrei Bursuc mentioned this new Challenge: Low-Power Image Recognition Challenge (LPIRC June 7, 2015), San Francisco, California, USA
Motivation
Many mobile systems (smartphones, electronic glass, autonomous robots) can capture images. These systems use batteries and energy conservation is essential. This challenge aims to discover the best technology in both image recognition and energy conservation. Winners will be evaluated based on both high recognition accuracy and low power usage.
Image Recognition
Image recognition involves many tasks. This challenge focuses on object detection, a basic routine in many recognition approaches. The following two examples illustrate the task. In the first example, there are two objects: a bird and a frog. In the second example, there are several objects: cars, persons, motorcycle, and a helmet. The training and validation data for LPIRC comes from the ImageNet Large Scale Visual Recognition Challenge detection competition. The test data will be specific to LPIRC.
FAQ (Frequently Asked Questions):
Q: Can I bring special hardware?
A: Yes.
Q: Can I use commodity hardware (such as mobile phone, laptop, or desktop)?
A: Yes.
Q: Can I combine special hardware and commodity hardware?
A: Yes.
Q: Are companies allow to compete?
A: Yes, of course.
Q: Are participants outside USA allowed?
A: Yes, of course.
Q: I want to spend a lot of money to build the best system and win. Is there any restriction on how much I can be spend?
A: Yes, you can spend as much money as you prefer.
Q: Must the system use DC power from batteries?
A: There is no such restriction. Your system may draw AC power from a wall socket.
Q: Is it possible that an image contain multiple objects?
A: Yes, that is possible
Q: Can I see without competing?
A: Yes.
Q: Is there any financial support?
A: Yes, travel grants are available. Please visit the conference web site (www.dac.com) for application.
Q: How does my program communicate with the system?
A: A program template will be available for the interface. The interface will be HTTP based. There will be a wireless/wired router.
Q: How should I write my program so that it can communicate with your program?
A: Please structure your program so that it has two components: the recognition engine and the interface. The interface will be released before the competition.
Q: How can my computer connect to yours, get data, and send results?
A: An intranet will be set up. Both wired (RJ 45) and wireless (Wifi) will be available.
Q: How do I know which Wifi network to connect to?
A: It will be announced before the competition. To prevent interference, the Wifi for this competition will make the SSID invisible and a password is needed.
Q: Can my computer connect to the Internet?
A: Yes. The San Francisco Convention Center has wireless network. Please be aware that your system needs to connect to an intranet to get the images and to send the results. If your system also needs access to the Internet, you will need a way (possibly two network interfaces) to connect to it.
Q: Will there be an opportunity to publish my method?
A: Yes. You are invited to submit your paper to a special issue in IEEE Transactions on Emerging Topics in Computing. The deadline is September 1, 2015.
Q: Is this the first competition of its kind?
A: We believe so, even though there have been related competitions. For example, Large Scale Visual Recognition Challenge at Image-Net concerns about only accuracy, not energy. The design contest at ISLPED does not focus on image recognition.
Q: My team has several people. Does everyone need to register?
A: Yes. Only people with registration can enter the room. This is a rule set by the convention center. The organizers are seeking fund to provide travel grants to every participant.Procedure
Before June 7, 2015
One June 7, 2015
- Please download the development toolkit.
- A skeleton program for the communication between your program and the referee program will be release in spring 2015. This program is HTTP based.
- Participants need to register at the DAC workshop. This is necessary because of the administrative cost. It is unnecessary to register the conference itself. If you need financial support, please apply for the travel grants. We will do our best to reimburse your travel expenses. The reimbursement will occur after the workshop.
After June 7, 2015
- Each system needs to enter an assigned web site to retrieve the test images. Each test image may contain objects in the 200 categories. When a contestant system starts retrieving the test images, power measurement starts.
- When the system finishes recognition, it sends the results (bounding boxes, object labels, and detection scores) to the assigned web site. Power measurement stops after all the results are received, or after the time limit is finished.
- The score is the ratio mAP/E, where mAP is mean across object categories of the average precision for detection of that category, and E is the total energy consumption. The participant that has the highest score wins the competition.
- In addition to the winner by the highest score, prizes will be given to the participants whose systems achieve (1) the least amount of energy with mAP at least one standard deviation above of the average for all participants and (2) the highest mAP with the energy is at least one standard deviation below the average of all participants.
- If a tie occurs, additional test images will be given until the tie is broken.
- After a time limit (currently set to 10 minutes) recording will stop and only results already transmitted will be counted.
- The winners will be announced on the next day (June 8) in the conference's Pavilion session. The winners will be given chances to briefly explain their approaches.
- All participants, as well as those that do not participate in this competition, are invited to submit papers to a special issue in IEEE Transactions on Emerging Topics in Computing. The submission deadline is September 1, 2015 and the papers will be published in September 2016.The submissions will be reviewed and it is not guaranteed that competition winners’ papers are accepted.
- Each winner is required to submit a report within three months explaining the method. The reports are not reviewed. If a winner submits a paper to the special issue mentioned above, the winner does not need to submit a separate report and the report will not be published immediately. If a winner does not submit a paper to the special issue, the report will be published immediately.
- If a winner fails to submit a report within three months, the prize will be recalled and given to the participant of the next highest score.
- If a participant does not wish to publish the method, the participant may choose so during registration. In this case, the participant's score will be announced and ranked but cannot receive any prize.
Prizes:
- The champion will receive $1,000.
- The second prize is $500.
- The third prize is $200.
- The prize for the least energy with high accuracy (at least one standard above the average) is $200.
- The prize for the highest accuracy with low energy (at least one standard below the average) is $200.
- Each winner will also receive one GPU card donated by Nvidia.
How are scores calculated?
The score has two parts: the accuracy in image recognition and the energy consumption.
Accuracy: To compute the accuracy, the bounding boxes returned by the system are sorted by putative category of object and by detection score. For each category of object, the bounding boxes are evaluated in order from highest detection score to lowest. If a bounding box has an intersection over union of greater than 0.5 with a ground truth bounding box that has not already been used to give a correct score to another bounding box, then it is marked correct with score 1, otherwise it is marked incorrect with score 0. After all boxes with detection score >= t have been evaluated the precision is the sum of the scores for each box divided by the number of boxes considered. Similarly the recall is the sum of the scores for the boxes returned divided by the total number of boxes of that category in the ground truth. The threshold t is varied to compute a precision vs recall curve. Average Precision (AP) is the average of precision over all recalls [0,1]. For more details on how mAP is computed, see this technical report ( http://arxiv.org/abs/1409.0575 ) on the ImageNet Challenge that includes details for how mAP is computed including how multiple detections for the same object are handled as well as a modification to the criteria for small bounding boxes (Section 4.3).
Energy: The energy will be measured by a power meter. The meter can measure both DC power and AC power. There are several options:
- A participant may use a tablet and disconnect a tablet's battery and connect the tablet with the battery with external wires. The power meter can measure the voltage and the current from the battery to calculate the power consumption.
- A participant may use a smartphone without removing the battery. In this case, the power consumed by the (AC to DC) charger is measured. This requires no change to the smartphone.
- A participant may use a desktop that draws AC power. The power from the socket is measured. This requires no change to the desktop.
As these examples describe, there is great flexibility in measuring energy consumption. Participants can decide which option is the most advantageous for the specific situation. Please be aware that the energy loss due to voltage conversion by the charger (or the power supply) is added to the total energy consumption and may reduce the overall score.
Date: 04 March 2015
Satellite: Rosetta
Depicts: Comet 67P/Churyumov-Gerasimenko
Copyright: ESA/Rosetta/NAVCAM, CC BY-SA IGO 3.0
Rosetta navigation camera (NavCam) image taken on 27 February 2015 at 98.2 km from the centre of comet 67P/Churyumov-Gerasimenko. The image measures 8.6 km across and has a scale of about 8.4 m/pixel.
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