Deep down the rabbit hole: CVPR 2015 and beyond

CVPR is the premier Computer Vision conference, and it's fair to think of it as the Olympics of Computer Vision Research. This year it was held in my own back yard -- less than a mile away from lovely Cambridge, MA!  Plenty of my MIT colleagues attended, but I wouldn't be surprised if Google had the largest showing at CVPR 2015. I have been going to CVPR almost every year since 2004, so let's take a brief tour at what's new in the exciting world of computer vision research.

Down the rabbit hole art by frostyshadows

A lot has changed. Nothing has changed. Academics used to be on top, defending their Universities and the awesomeness happening inside their non-industrial research labs. Academics are still on top, but now defending their Google, Facebook, Amazon, and Company X affiliations. And with the hiring budget to acquire the best and a heavy publishing-oriented culture, don't be surprised if the massive academia exodus continues for years to come. It's only been two weeks since CVPR, and Google has since then been busy making ConvNet art, showing the world that if you want to do the best Deep Learning research, they are King.

An army of PhD students and Postdocs simply cannot defeat an army of Software Engineers and Research Scientists. Back in the day, students used to typically depart after a Computer Vision PhD (there used to be few vision research jobs and Wall Street jobs were tempting). Now the former PhD students run research labs at big companies which have been feverishly getting into vision. It seems there aren't enough deep experts to fill the deep demand.

Datasets used to be the big thing -- please download my data!  Datasets are still the big thing -- but we regret to inform you that your university’s computational resources won’t make the cut (but at Company X we’re always hiring, so come join us, and help push the frontier of research together).

Related Article: Under LeCun's Leadership, Facebook's AI Research Lab is beefing up their research presence

If you want to check out the individual papers, I recommend Andrej Karpathy's online navigation tool for CVPR 2015 papers or take a look at the vanilla listing of CVPR 2015 papers on the CV foundation website. Zoya Bylinskii, an MIT PhD Candidate, also put together a list of interesting CVPR 2015 papers.

The ConvNet Revolution: There's a pre-trained network for that

Machine Learning used to be the Queen. Machine Learning is now the King. Machine Learning used to be shallow, but today's learning approaches are so deep that the diagrams barely fit on a single slide. Grad students used to pass around jokes about Yann LeCun and his insistence that machine learning will one day do the work of the feature engineering stage. Now it seems that the entire vision community gets to ignore you when you insist that “manual feature engineering” is going to save the day. Yann LeCun gave a keynote presentation with the intriguing title "What's wrong with Deep Learning" and it seems that Convolutional Neural Networks (also called CNNs or ConvNets) are everywhere at CVPR.

Figure from Karpathy's Convolutional Neural Network tutorial

It used to be hard to publish ConvNet research papers at CVPR, it's now hard to get a CVPR paper if you didn't at least compare against a ConvNet baseline. Got a new cool problem? Oooh, you didn’t try a ConvNet-based baseline? Well, that explains why nobody cares.

But it's not like the machines are taking over the job of the vision scientist. Today's vision scientist is much more of an applied machine learning hacker than anything else, and because of the strong CNN theme, it is much easier to understand and re-implement today's vision systems. What we're seeing at CVPR is essentially a revisiting of the classic problems like segmentation and motion, using this new machinery. As Samson Timoner phrased it at the local Boston Vision Meetup, when Mutual Information was popular, the community jumped on that bandwagon -- it's ConvNets this time around. But it's not just a trend, the non-CNN competition is getting crushed.

Figure from Bharath Hariharan's Hypercolumns CVPR 2015 paper on segmentation using CNNs

There's still plenty to be done by a vision scientist, and a solid formal education in mathematics is more important than ever. We used to train via gradient descent. We still train via gradient descent. We used to drink Coffee, now we all drink Caffe. But behind the scenes, it is still mathematics.

Related Page: Caffe Model Zoo where you can download lots of pretrained ConvNets

Deep down the rabbit hole

CVPR 2015 reminds of the pre-Newtonian days of physics. A lot of smart scientists were able to predict the motions of objects using mathematics once the ingenious Descartes taught us how to embed our physical thinking into a coordinate system. And it's pretty clear that by casting your computer vision problem in the language of ConvNets, you are going to beat just about anybody doing computer vision by hand. I think of Yann LeCun (one of the fathers of Deep Learning) as a modern day Descartes, only because I think the ground-breaking work is right around the corner. His mental framework of ConvNets is like a much needed coordinate system -- we might not know what the destination looks like, but we now know how to build a map.

Deep Networks are performing better every month, but I’m still waiting for Isaac to come in and make our lives even easier. I want a simplification. But I'm not being pessimistic -- there is a flurry of activity in the ConvNet space for a very good reason (in case you didn't get to attend CVPR 2015), so I'll just be blunt: ConvNets fuckin' work! I just want the F=ma of deep learning.

Open Source Deep Learning for Computer Vision: Torch vs Caffe

CVPR 2015 started off with some excellent software tutorials on day one.  There is some great non-alpha deep learning software out there and it has been making everybody's life easier.  At CVPR, we had both a Torch tutorial and a Caffe tutorial.  I attended the DIY Deep Learning Caffe tutorial and it was a full house -- standing room only for slackers like me who join the party only 5 minutes before it starts. Caffe is much more popular that Torch, but when talking to some power users of Deep Learning (like +Andrej Karpathy and other DeepMind scientists), a certain group of experts seems to be migrating from Caffe to Torch.

Caffe is developed at Berkeley, has a vibrant community, Python bindings, and seems to be quite popular among University students. Prof. Trevor Darrell at Berkeley is even looking for a Postdoc to help the Caffe effort. If I was a couple of years younger and a fresh PhD, I would definitely apply.

Instead of following the Python trend, Torch is Lua-based. There is no need for an interpreter like Matlab or Python -- Lua gives you the magic console. Torch is heavily used by Facebook AI Research Labs and Google's DeepMind Lab in London.  For those afraid of new languages like Lua, don't worry -- Lua is going to feel "easy" if you've dabbled in Python, Javascript, or Matlab. And if you don't like editing protocol buffer files by hand, definitely check out Torch.

It's starting to become clear that the future power of deep learning is going to come with its own self-contained software package like Caffe or Torch, and not from a dying breed of all-around tool-belts like OpenCV or Matlab. When you share creations made in OpenCV, you end up sharing source files, but with the Deep Learning toolkits, you end up sharing your pre-trained networks.  No longer do you have to think about a combination of 20 "little" algorithms for your computer vision pipeline -- you just think about which popular network architecture you want, and then the dataset.  If you have the GPUs and ample data, you can do full end-to-end training.  And if your dataset is small/medium, you can fine-tune the last few layers. You can even train a linear classifier on top of the final layer, if you're afraid of getting your hands dirty -- just doing that will beat the SIFTs, the HOGs, the GISTs, and all that was celebrated in the past two decades of computer vision.

Related Article: Torch vs Theano on fastml.com
Related Code: Andrea Vedaldi's MatConvNet Deep Learning Library for MATLAB users

The way in which ConvNets are being used at CVPR 2015 makes me feel like we're close to something big.  But before we strike gold, ConvNets still feel like a Calculus of Shadows, merely "hoping" to get at something bigger, something deeper, and something more meaningful. I think the flurry of research which investigates visualization algorithms for ConvNets suggests that even the network architects aren't completely sure what is happening behind the scenes.

The Video Game Engine Inside Your Head: A different path towards Machine Intelligence

Josh Tenenbaum gave an invited talk titled The Video Game Engine Inside Your Head at the Scene Understanding Workshop on the last day of the CVPR 2015 conference. You can read a summary of his ideas in a short Scientific American article. While his talk might appear to be unconventional by CVPR standards, it is classic Tenenbaum. In his world, there is no benchmark to beat, no curves to fit to shadows, and if you allow my LeCun-Descartes analogy, then in some sense Prof. Tenenbaum might be the modern day Aristotle. As Prof. Jianxiong Xiao introduced Josh with a grand intro, he was probably right -- this is one of the most intelligent speakers you can find.  He speaks 100 words a second, you can't help but feel your brain enlarge as you listen.

One of Josh's main research themes is going beyond the shadows of image-based recognition.  Josh's work is all about building mental models of the world, and his work can really be thought of as analysis-by-synthesis. Inside his models is something like a video game engine, and he showed lots of compelling examples of inferences that are easy for people, but nearly impossible for the data-driven ConvNets of today.  It's not surprising that his student is working at Google's DeepMind this summer.

A couple of years ago, Probabilistic Graphical Models (the marriage of Graph Theory and Probabilistic Methods) used to be all the rage.  Josh gave us a taste of Probabilistic Programming, and while we're not yet seeing these new methods dominate the world of computer vision research, keep your eyes open. He mentioned a recent Nature paper (citation below) from another well respected machine intelligence research, which should keep the trendsetters excited for quite some time. Just take a look at the bad-ass looking Julia code below:

Probabilistic machine learning and artificial intelligence. Zoubin Ghahramani. Nature 521, 452–459 (28 May 2015) doi:10.1038/nature14541

To see some of Prof. Tenenbaum's ideas in action, take a look at the following CVPR 2015 paper, titled Picture: A Probabilistic Programming Language for Scene Perception. Congrats to Tejas D. Kulkarni, the first author, an MIT student, who got the Best Paper Honorable Mention prize for this exciting new work. Google DeepMind, you're going to have one fun summer.

Picture: A Probabilistic Programming Language for Scene Perception

Object Detectors Emerge in Deep Scene CNNs

There were lots of great presentation as the Scene Understanding Workshop, and another talk that truly stood out was about a new large-scale dataset (MIT Places) and a thorough investigation of what happens when you train with scenes vs. objects.

Antonio Torralba from MIT gave the talk about the Places Database and an in-depth analysis of what is learned when you train on object-centric databases like ImageNet vs. Scene-scentric databases like MIT Places. You can check out "Object Detectors Emerge" slides or their ArXiv paper to learn more. Great work by an upcoming researcher, Bolei Zhou!

Overheard at CVPR: ArXiv Publishing Frenzy & Baidu Fiasco 

In the long run, the recent trend of rapidly pushing preprints to ArXiv.org is great for academic and industry research alike. When you have a large collection of experts exploring ideas at very fast rates, waiting 6 months until the next conference deadline just doesn't make sense.  The only downside is that it makes new CVPR papers feel old. It seems like everybody has already perused the good stuff the day it went up on ArXiv. But you get your "idea claim" without worrying that a naughty reviewer will be influenced by your submission. Double blind reviewing, get ready for a serious revamp.  We now know who's doing what, significantly before publication time.  Students, publish-or-perish just got a new name. Whether the ArXiv frenzy is a good or a bad thing, is up to you, and probably more a function of your seniority than anything else. But the CV buzz is definitely getting louder and will continue to do so.

The Baidu cheating scandal might appear to be big news for outsiders just reading the Artificial Intelligence headlines, but overfitting to the testing set is nothing new in Computer Vision. Papers get retracted, grad students often evaluate their algorithms on test sets too many times, and the truth is that nobody's perfect.  When it's important to be #1, don't be surprised that your competition is being naughty. But it's important to realize the difference between ground-breaking research and petty percentage chasing. We all make mistakes, and under heavy pressure, we're all likely to show our weaknesses.  So let's laugh about it.  Let's hire the best of the best, encourage truly great research, and stop chasing percentages.  The truth is that a lot of the top performing methods are more similar than different.


Conclusion
CVPR has been constantly growing in attendance. We now have Phd Students, startups, Professors, recruiters, big companies, and even undergraduates coming to the show. Will CVPR become the new SIGGRAPH?

CVPR attendance plot from Changbo Hu. 

ConvNets are here to stay, but if we want ConvNets to be more than than a mere calculus of shadows, there's still ample work do be done. Geoff Hinton's capsules keep popping up during midnight discussions. "I want to replace unstructured layers with groups of neurons that I call 'capsules' that are a lot more like cortical columns" -- Geoff Hinton during his Reddit AMA. A lot of people (like Prof. Abhinav Gupta from CMU) are also talking about unsupervised CNN training, and my prediction is that learning large ConvNets from videos without annotations is going to be big at next year's CVPR.

Most importantly, when the titans of Deep Learning get to mention what's wrong with their favorite methods, I only expect the best research to follow. Happy computing and remember, never stop learning.

Deep Learning vs Big Data: Who owns what?

In order to learn anything useful, large-scale multi-layer deep neural networks (aka Deep Learning systems) require a large amount of labeled data. There is clearly a need for big data, but only a few places where big visual data is available. Today we'll take a look at one of the most popular sources of big visual data, peek inside a trained neural network, and ask ourselves some data/model ownership questions. The fundamental question to keep in mind is the following, "Are the learned weights of a neural network derivate works of the input images?" In other words, when deep learning touches your data, who owns what?



Background: The Deep Learning "Computer Vision Recipe"
One of today's most successful machine learning techniques is called Deep Learning. The broad interest in Deep Learning is backed by some remarkable results on real-world data interpretation tasks dealing with speech[1], text[2], and images[3]. Deep learning and object recognition techniques have been pioneered by academia (University of Toronto, NYU, Stanford, Berkeley, MIT, CMU, etc), picked up by industry (Google, Facebook, Snapchat, etc), and are now fueling a new generation of startups ready to bring visual intelligence to the masses (Clarifai.com, Metamind.io, Vision.ai, etc). And while it's still not clear where Artificial Intelligence is going, Deep Learning will be a key player.

Related blog post: Deep Learning vs Machine Learning vs Pattern Recognition
Related blog post: Deep Learning vs Probabilistic Graphical Models vs Logic

For visual object recognition tasks, the most popular models are Convolutional Neural Networks (also known as ConvNets or CNNs). They can be trained end-to-end without manual feature engineering, but this requires a large set of training images (sometimes called big data, or big visual data). These large neural networks start out as a Tabula Rasa (or "blank slate") and the full system is trained in an end-to-end fashion using a heavily optimized implementation of Backpropagation (informally called "backprop"). Backprop is nothing but the chain rule you learned in Calculus 101 and today's deep neural networks are trained in almost the same way they were trained in the 1980s. But today's highly-optimized implementations of backprop are GPU-based and can process orders of magnitude more data than was approachable in the pre-internet pre-cloud pre-GPU golden years of Neural Networks. The output of the deep learning training procedure is a set of learned weights for the different layers defined in the model architecture -- millions of floating point numbers representing what was learned from the images. So what's so interesting about the weights? It's the relationship between the weights and the original big data, that will be under scrutiny today.

"Are weights of a trained network based on ImageNet a derived work, a cesspool of millions of copyright claims? What about networks trained to approximate another ImageNet network?"
[This question was asked on HackerNews by kastnerkyle in the comments of A Revolutionary Technique That Changed Machine Vision.]

In the context of computer vision, this question truly piqued my interest, and as we start seeing robots and AI-powered devices enter our homes I expect much more serious versions of this question to arise in the upcoming decade. Let's see how some of these questions are being addressed in 2015.

1. ImageNet: Non-commercial Big Visual Data

Let's first take a look at the most common data source for Deep Learning systems designed to recognize a large number of different objects, namely ImageNet[4]. ImageNet is the de-facto source of big visual data for computer vision researchers working on large scale object recognition and detection. The dataset debuted in a 2009 CVPR paper by Fei-Fei Li's research group and was put in place to replace both PASCAL datasets (which lacked size and variety) and LabelMe datasets (which lacked standardization). ImageNet grew out of Caltech101 (a 2004 dataset focusing on image categorization, also pioneered by Fei-Fei Li) so personally I still think of ImageNet as something like "Stanford10^N". ImageNet has been a key player in organizing the scale of data that was required to push object recognition to its new frontier, the deep learning phase.

ImageNet has over 15 million images in its database as of May 1st, 2015.

Problem: Lots of extremely large datasets are mined from internet images, but these images often come with their own copyright.  This prevents collecting and selling such images, and from a commercial point of view, when creating such a dataset, some care has to be taken.  For research to keep pushing the state-of-the-art on real-world recognition problems, we have to use standard big datasets (representative of what is found in the real-world internet), foster a strong sense of community centered around sharing results, and maintain the copyrights of the original sources.

Solution: ImageNet decided to publicly provide links to the dataset images so that they can be downloaded without having to be hosted on an University-owned server. The ImageNet website only serves the image thumbnails and provides a copyright infringement clause together with instructions where to file a DMCA takedown notice. The dataset organizers provide the entire dataset only after signing a terms of access, prohibiting commercial use. See the ImageNet clause below (taken on May 5th, 2015).

"ImageNet does not own the copyright of the images. ImageNet only provides thumbnails and URLs of images, in a way similar to what image search engines do. In other words, ImageNet compiles an accurate list of web images for each synset of WordNet. For researchers and educators who wish to use the images for non-commercial research and/or educational purposes, we can provide access through our site under certain conditions and terms."

2. Caffe: Unrestricted Use Deep Learning Models

Now that we have a good idea where to download big visual data and an understanding of the terms that apply, let's take a look at the the other end of the spectrum: the output of the Deep Learning training procedure. We'll take a look at Caffe, one of the most popular Deep Learning libraries, which was engineered to handle ImageNet-like data.  Caffe provides an ecosystem for sharing models (the Model Zoo), and is becoming an indispensable tool for today's computer vision researcher. Caffe is developed at the Berkeley Vision and Learning Center (BVLC) and by community contributors -- it is open source.

Slide from DIY Deep Learning for Vision with Caffe

Problem: As a project that started at a University, Caffe's goal is to be the de-facto standard for creating, training, and sharing Deep Learning models. The shared models were initially licensed for non-commercial use, but the problem is that a new wave of startups is using these techniques, so there must be a licensing agreement which allows Universities, large companies, and startups to explore the same set of pretrained models.

Solution: The current model licensing for Caffe is unrestricted use. This is really great for a broad range of hackers, scientists, and engineers.  The models used to be shared with a non-commercial clause. Below is the entire model licensing agreement from the Model License section of Caffe (taken on May 5th, 2015).

"The Caffe models bundled by the BVLC are released for unrestricted use. 

These models are trained on data from the ImageNet project and training data includes internet photos that may be subject to copyright. 

Our present understanding as researchers is that there is no restriction placed on the open release of these learned model weights, since none of the original images are distributed in whole or in part. To the extent that the interpretation arises that weights are derivative works of the original copyright holder and they assert such a copyright, UC Berkeley makes no representations as to what use is allowed other than to consider our present release in the spirit of fair use in the academic mission of the university to disseminate knowledge and tools as broadly as possible without restriction." 

3. Vision.ai: Dataset generation and training in your home 

Deep Learning learns a summary of the input data, but what happens if a different kind of model memorizes bits and pieces of the training data? And more importantly what if there are things inside the memorized bits which you might not want shared with outsiders?  For this case study, we'll look at Vision.ai, and their real-time computer vision server which is designed to simultaneously create a dataset and learn about an object's appearance. Vision.ai software can be applied to real-time training from videos as well as live webcam streams.

Instead of starting with big visual data collected from internet images (like ImageNet), the vision.ai training procedure is based on a person waving an object of interest in front of the webcam. The user bootstraps the learning procedure with an initial bounding box, and the algorithm continues learning hands-free. As the algorithm learns, it is stores a partial history of what it previously saw, effectively creating its own dataset on the fly. Because the vision.ai convolutional neural networks are designed for detection (where an object only occupies a small portion of the image), there is a large amount of background data presented inside the collected dataset. At the end of the training procedure you get both the Caffe-esque bit (the learned weights) and the ImageNet bit (the collected images). So what happens when it's time to share the model?

A user training a cup detector using vision.ai's real-time detector training interface

Problem: Training in your home means that potentially private and sensitive information is contained inside the backgrounds of the collected images. If you train in your home and make the resulting object model public, think twice about what you're sharing. Sharing can also be problematic if you have trained an object detector from a copyrighted video/images and want to share/sell the resulting model.

Solution: When you save a vision.ai model to disk, you get both a compiled model and the full model. The compiled model is the full model sans the images (thus much smaller). This allows you to maintain fully editable models on your local computer, and share the compiled model (essentially only the learned weights), without the chance of anybody else peeking into your living room. Vision.ai's computer vision server called VMX can run both compiled and uncompiled models; however, only uncompiled models can be edited and extended. In addition, vision.ai provides their vision server as a standalone install, so that all of the training images and computations can reside on your local computer. In brief, vision.ai's solution is to allow you to choose whether you want to run the computations in the cloud or locally, and whether you want to distribute full models (with background images) or the compiled models (solely what is required for detection). When it comes to sharing the trained models and/or created datasets, you are free to choose your own licensing agreement.

4. Open Problems for Licensing Memory-based Machine Learning Models

Deep Learning methods aren't the only techniques applicable to object recognition. What if our model was a Nearest-Neighbor classifier using raw RGB pixels? A Nearest Neighbor Classifier is a memory based classifier which memorizes all of the training data -- the model is the training data. It would be contradictory to license the same set of data differently if one day it was viewed as training data and another day as the output of a learning algorithm. I wonder if there is a way to reconcile the kind of restrictive non-commercial licensing behind ImageNet with the unrestricted licensing use strategy of Caffe Deep Learning Models. Is it possible to have one hacker-friendly data/model license agreement to rule them all?

Conclusion

Don't be surprised if neural network upgrades come as part of your future operating system. As we transition from a data economy (sharing images) to a knowledge economy (sharing neural networks), legal/ownership issues will pop up. I hope that the three scenarios I covered today (big visual data, sharing deep learning models, and training in your home) will help you think about the future legal issues that might come up when sharing visual knowledge. When AI starts generating its own art (maybe by re-synthesizing old pictures), legal issues will pop up. And when your competitor starts selling your models and/or data, legal issues will resurface. Don't be surprised if the MIT license vs. GPL license vs. Apache License debate resurges in the context of pre-trained deep learning models. Who knows, maybe AI Law will become the next big thing.

References
[1] Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learning: NVIDIA dev blog post about Baidu's work on speech recognition using Deep Learning. Andrew Ng is working with Baidu on Deep Learning.

[2] Text Understanding from Scratch: Arxiv paper from Facebook about end-to-end training of text understanding systems using ConvNets. Yann Lecun is working with Facebook on Deep Learning.

[3] ImageNet Classification with Deep Convolutional Neural Networks. Seminal 2012 paper from the Neural Information and Processing Systems (NIPS) conference which showed breakthrough performance from a deep neural network. Paper came out of University of Toronto, but now most of these guys are now at Google.  Geoff Hinton is working with Google on Deep Learning.

[4] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li and L. Fei-Fei, ImageNet: A Large-Scale Hierarchical Image Database. IEEE Computer Vision and Pattern Recognition (CVPR), 2009.

Jia Deng is now assistant professor at Michigan University and he is growing his research group. If you're interested in starting a PhD in deep learning and vision, check out his call for prospective students. This might be a younger version of Andrew Ng.

Richard Socher is the CTO and Co-Founder of MetaMind, and new startup in the Deep Learning space. They are VC-backed and have plenty of room to grow.

Jia Li is now Head of Research at Snapchat, Inc. I can't say much, but take a look at the recent VentureBeat article: Snapchat is quietly building a research team to do deep learning on images, videos. Jia and I overlapped at Google Research back in 2008.

Fei-Fei Li is currently the Director of the Stanford Artificial Intelligence Lab and the Stanford Vision Lab. See the article on Wired: If we want our machines to think, we need to teach them to see. Yann, you have some competition.

Yangqing Jia created the Caffe project during his PhD at UC Berkeley. He is now a research scientist at Google.

Tomasz Malisiewicz is the Co-Founder of Vision.ai, which focuses on real-time training of vision systems -- something which is missing in today's Deep Learning systems. Come say hi at CVPR.

Venture Pitch Contest at CVPR 2015 in Boston, MA

This year's CVPR will be in Boston, and as always, I expect it to be the single best venue to meet computer vision experts and see cutting edge research. I expect Google and Facebook to show off their best Deep Learning systems, NVIDIA to demo their newest GPUs, and dozens of computer vision startups to be looking for talent to grow their teams.

I expect the entrepreneur/academic ratio to be much higher, as it is getting easier for PhD students and postdocs to start their own companies.  This year's CVPR will even feature a Venture Pitch Contest as part of the Fourth Annual Vision Industry and Entrepreneur (VIEW) Workshop at CVPR. From the VIEW workshop webpage:

Computer vision as a technology is penetrating the industry at an extraordinary pace with many computer vision applications directly becoming consumer commodities. Both startups and big companies have contributed to this trend. At the fourth annual Vision Industry and Entrepreneur Workshop, we are organizing a first of its kind Startup Pitch Contest. As a computer vision innovator, this is your chance to present the next great computer vision product idea to a distinguished panel of judges which will include Venture Capitalists, Investors and leading Researchers in the field.

Applications should employ novel computer vision technologies towards an innovative product. The best submissions would be selected for an Elevator Pitch presentation in front of the judges. Prizes would be awarded to the winners who would be announced at the end of the workshop. The details about the judging criteria will be posted on the website.

The submission is broken into two phases – Preliminary submission consisting of a title and an abstract, and, Final submission consisting of a one page summary with technology overview, feasibility, outreach (customers and market size) and monetization (business model). The summary should be tailored at soliciting funding from sources such as venture capital to invest in the idea. The applicants should indicate whether they are academic researchers or industry professionals. Only non-confidential material may be submitted.

Even if you're not ready to pitch, you can submit a poster or demo to the Industry Session part of the VIEW 2015 Workshop. Great place to show off your new computer vision-powered app.  One of the organizers, Samson Timoner, told me the deadlines for submission have been extended. Here are the new dates:

Submission: April 3, 2015 (extended)
Notification: April 8, 2015 (extended)
Workshop: June 11, 2015

This year's CVPR is going to be a great place to network with startups, share ideas, see cutting-edge research and (NEW in 2015) meet folks from the venture capital world. Who knows, if I'm there, I might be wearing a vision.ai T-shirt.