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Vidrovr, NYC Media Lab Combine Demo Day
- 3. Videos Today Quantity 400+ Hours of video uploaded per minute to YouTube 2B Views of videos per month for a media company
- 4. 217M U.S. viewers of digital video 78.4% Internet users who watch video Videos Today Viewers
- 5. $6B Spent on online video ads (2016) 38% Share of online videos watched which are ads Videos Today Revenue
- 6. Problem
- 7. But, video content is REALLY hard to search and leverage Companies are sitting on a gold mine of video content…
- 8. The Reasons Manual metatagging is expensive and time-consuming. Internal video content management systems (CMS) and recommendation software underwhelm.
- 12. Metadata Generation “I literally have conference rooms filled with people who tag our videos all day. I want my conference rooms back!” - Media Executive
- 13. Search & Recommendation “We’ve tried a lot of services, but we haven’t been able to find a CMS that allows us to quickly find and use our video clips.” - Video Management, News Company
- 14. Automatic video metadata generation APIs in AWS | GCE Editor suite to integrate various in-house media Plugins to make it seamless (Browsers + CMS) Linking videos to social media (UGC) Hashtag-content linking Leveraging customer insight Shadowing writers+ editors Addressable Needs
- 15. Our Solution
- 16. News Rover Analog or digital ingestion 100 hours of video per day 12|100 simultaneous channels Server Pipeline Event detection Visual speaker detection Cross media analysis Story linking ML + CV Awards 1st Place Grand Prize ACM MM 2nd place NYC Media Lab Annual Summit Demo Competition Best Demo Greater New York Area Multimedia and Vision Meeting Demo: http://bit.ly/1Warukc *Patented by Columbia APIs, Front end Front facing APIs / stack Visualizations
- 18. Market
- 19. Manual service currently being paid by a media company: $360,000 | month ~50 contracts $216M | year Metadata Generation
- 20. Standard CMS for company: 3M/year $150M | year Media/Video CMS ~50 companies
- 21. 21.9% 5 year CAGR 18x CTR Over banner ads x2 $/Ad Video ads cost way more Format Key to Mobile & Social engagement Online Video Ads 2013 2015 2017 2019 3 6 9 12 Billions
- 24. Customized Deep Learning Solutions Multi-Source and Trending Tags Rapid Deployment Extensible to Specific Domains Private Protected Data man, business, campaign, U.S.A., politics Partners
- 25. Team
- 26. Co-Founder Technical AdvisorCo-Founder Education: BS+BA University of San Diego MS/PhD Columbia University NSF GFRP Fellow Education: BS University of California, LA MS/PhD Columbia University NSF GFRP Fellow Richard Dicker Professor Computer Science and EE Senior Vice Dean Leading researcher in multimedia, computer vision, and machine learning since 1990’s Corporate Experience: Corporate Experience: Skills: Computer Vision Machine Learning Software Development Skills: Machine Learning Software Development User-Interfaces Management Team
- 27. Special Thanks to Our Columbia Colleagues: Brendan Jou and Hongzhi Li Our Designer and Branding Specialist: Shakti MB contact@vidrovr.com
Editor's Notes
- Let’s get into it.
- If there are any really young people in here they might not have any idea what this picture is, but most of us, me including will know exactly that this is a video store. Renting a movie from the movie rental store was great, mainly because of the movie buffs who worked there. The video store clerk had two things going for them 1. they had an encyclopedic knowledge of where each movie was in the store (because they had watched all of them) and 2. if you told them a movie you liked in the past or gave them a clue as to how you were feeling could find the perfect movie for you that night.
- Now let’s fast forward to today, it would be totally impossible for anyone to watch all of the videos that are available online in company archives. Over 400 hours of video are uploaded to Youtube every minute, and it’s not all junk. One media company who produces videos is averaging 2 Billion views of their online videos per month. http://www.statista.com/topics/1137/online-video/ http://www.reelseo.com/buzzfeed-video-strategy/
- And it’s not just the fact that we have lots of videos online now, people are also watching those videos rabidly. Over 200 million people in America alone watch videos online frequently, and almost 80% of internet users use it to watch video. http://www.statista.com/topics/1137/online-video/
- And where there is content and viewers your going to get money. Last year 6$ billion dollars were spent in advertising on online videos, which includes mobile and internet ads, and even more surprising to us is that 38% of the videos viewed by people are online ads in the form of pre-roll or other videos. Companies and advertisers understand that online videos are one of their most valuable commodities. http://www.statista.com/topics/1137/online-video/
- This all sounds great, tons of video, people are watching, lots of money, so what’s the problem?
- The problem is that there is no movie buff video store clerk for online video. Whereas the video clerk knew what was in all the movies in his or her store and how to recommend the movies for you this is currently not possible for a companies video assets. This makes video content really hard to search and leverage.
- The main reasons this is a challenge for companies is two-fold. 1. Sufficient manual metatagging is expensive and very time-consuming to perform, it takes a lot of human resources to complete tagging of what is in the videos, and there is no standard way to perform this operation. and 2. Internal Content Management Systems for videos performing search and recommendation consistency underwhelm across almost every customer that we have spoken to because they lack the ability to find granular pieces on vital information.
- We aren’t making this stuff up either. We have done over 110 interviews with these companies and many more discussing these issues and are now in negotiations with some of them as to how our technologies can help them solve their problems with leveraging videos.
- So now that I have told you about the problems that these companies have, let’s talk about the solutions that are currently being implemented to solve these issues.
- To generate the metadata for videos, companies have taken an age old approach to solve this problem. Hire a ton of people to watch and tag videos, whether it be external or internal hires, people are watching videos and tagging them. This is a great quote from one media executive we spoke with that really details the issue. “I literally have conference rooms filled with people who tag our videos all day. I want my conference rooms back!”
- Once the metadata is generated we then need to be able to build systems that are able to search and recommend video content quickly and seamlessly. This is a real pain point for almost all of the media companies that we have spoken to. Leveraging videos is a real issue. This quote sums up pretty well the issues that many companies are having “We’ve tried a lot of services, but we haven’t been able to find a CMS that allows us to quickly find and use our video clips.”
- It’s clear now that we are in a different era than when we could walk into a video store and have a lovely experience where the store attendant knows what’s in each video and can recommend one you will like with a high level of consistency. Our goal with vidrovr is to change that. We see three particular addressable needs that are seen throughout the media industry, and they are as follows. Companies need quick automatic ways to generate searchable metadata for their videos, preferably in the cloud. Video producers, editors, and the people creating content need a set of tools to help them search and recommend videos to them on the fly, and finally the same writers, journalists, and editors need some way to automatically search through social media content and bring related content in the social sphere to light.
- Now I’ll let Dan take it from here to finish out the rest of our presentation. Thank you Joe. Before jumping into the new prototype demo we have built in the last few weeks, I would like touch on the patented CV and ML technologies we have developed over the last 3 years at Columbia. 13s
- News Rover is a system that monitors and processes 100 hours of television news a day. We use Machine Learning and Computer Vision Technologies to extract important pieces of information within the video stream which I will highlight for you in our demo. We have built a video editing suite that automatically recommends videos and social media for a digital product. Let’s take a look.
- Imagine you’re writing a story about our most popular presidential candidate - Donald Trump and the Supreme court nomination. You can select and highlight some text , and our system will discover relevant video clips from all of the television news that we have collected for the last 3 years. Our system has automatically removed all commercials, clipped and segmented larger news programs into smaller sized story chunks related to the highlighted text. Let’s select one to see more information. You receive the full video to watch. This one appears to be from the CNN Newsroom about the supreme court nomination. With our technology we are able to detect who is speaking on screen in a video, let’s take a look at what we have found. By clicking on Donald Trump we can directly hop to the section where he is speaking on screen, and do the same for Ted Cruz Working with our partner Axon Image we are able to extract visual concepts directly from the frames of the video, lets see what we get. Things like court, scale, politics and justice appear throughout this video. We can also automatically find and recommend hashtags and user generated content from social media related to this video. A cool example here is #doyourjob . This hashtag turns out to be an attempt to influence the Senate the hear the Supreme court nomination. We can then automatically embed any of this content into the story. All this was all done completely automatically.
- So what’s our market. Well the last 2.5 months we have devoted to figuring that out and here is what we came up with. I will go through them chronologically in the same order we are currently planning to enter these respective markets.
- First off we plan on replacing those offices of people that Joe showed you earlier that do manual tagging. One company we talked to is paying at least $120/video hr for this service. They hire a 3rd party company that sources thousands of taggers to annotate their video. This company is not unique. From government to sports organizations video collections are everywhere. We estimate that world wide there are at least 50 such contracts and this makes for a 432 M dollar market.
- As you saw we plan also on providing a video centric CMS integration tool, not necessarily replacing the CMS but integrating with it directly to provide both video recommendation and UGC sourcing for content makers. Large media companies pay big dollars either to build their own CMSs or to use someone else’s, but these CMSs provide very few video searching and indexing resources. There are at least 50 such companies and this makes for a low 150M estimate.
- Finally, we have been approached by a number of ad agencies interested in targeting ads based on online video content, instead of simply user information. We are currently talking with people who are interested in applying our technologies in this space This space has a very large potential for exponential growth.
- And now I would like to quickly mention how we differentiate ourselves from our competitors and who we are partnering with.
- We have 2 sets of competitors. First let me address the CMS companies. The majority of CMS providers handling video do so as add on services, and do not focus on it primarily. Secondly there are companies attempting to solve general visual tagging, primarily using deep learning. We believe a one size fits all solution that these companies are working towards is a ways off. But this does not preclude building commercially viable systems today by leveraging domain-specific information, much like we did with News Rover.
- To leverage some of the deep learning capabilities without dedicating a lot of time while part of the Combine, we have partnered with Axon Image, who specializing in DL visual applications.
- Finally. I would like to mention who we are.
- The management team is made up of current PhD students Joe Ellis and myself as the Co-Founders of the company. Both of us have corporate industry experience at companies such as Google, Janelia, NASA, and IBM and are currently NSF Fellows. The company is advised by Professor Shih-Fu Chang, who is a leading researcher in multimedia, computer vision, and machine learning.
- We have opened negotiations with a couple of media companies, and we have incorporated this week.