Rip Y Celeb

[Margit Szermer]

NowIP65-rated to handle whatever elemental challenges your location brings, Celeb IKON incorporates our latest technical innovations in a relaunched series bearing the name of our most celebrated LED soft lights.

Confidently move from warm to daylight tones and solve color rendering challenges with an LED system equipped with two full-spectrum white sources designed to deliver the broadest spectrum white light.

Celeb IKON excels not only as a full-spectrum white light generator that reflects the broadest range of color back to camera, but also as a color projector delivering the widest gamut of the brightest colors. Powered by an innovative approach that takes our pursuit of color science to a new level of color performance.

Fitted with our five-emitter R-G-B-WW-CW LED configuration, Celeb IKON brings a tremendous boost in light output, maintains consistent output levels at all color points, and offers improved spectrum whites managed by our patented True Match color science.

True Match gives you access to the exact look you want to achieve on camera. Instantly dominate the close-ups with fantastic color-temperature, camera lut and dimming options. Access full saturated colors with hue angle color adjustments and input RGB values.

True Match makes on-board control quick and easy for board operators to remotely configure and operate Celeb IKON via RDM and DMX. LumenRadio wireless DMX comes standard, with the option to choose Multiverse wireless DMX at the time of purchase or change any time through our service center.

CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations, including

The final evaluation has been finished on 6/17. Among the 19 registered teams, 14 teams have successfully finished the task during the evaluation time window. We are now calculating the coverage/precision numbers and will send them out shortly (by 6/21) after verification, together with the final ranks.

Final evaluation started on 6/13, more than half of the teams have finished the first round of evaluation requests. Please keep your recognizer online, for extra verification/retry requests.

If your recognizer has encountered issues during the first round of evaluation. Please ping us for another round of request. We can send you up to 3 rounds of evaluation traffic, as long as time allows. But keep in mind, you can only choose one of the results for ranking. By default, we will use your last result.

More teams passed dry run (format, speed, and accuracy) and are ready for final evaluation. We are sending out the coverage/precision numbers to each passed team. Latest update about dry run progress is shown below.

As before, we will use an open multimedia hub (Prajna Hub) for evaluation. That is, you need to register your recognizer to Prajna Hub, which essentially turns your recognition program to a cloud service. Then your algorithm can be evaluated remotely.Note that your algorithm is still running on your local machine and you have full controls on it. This update provides:

With the success of previous MSR Image Retrieval/Recognition Challenges (MSR IRC) at IEEE ICME, ACM Multimedia 2014 and 2015, Microsoft Research is happy to announce MSR IRC at ACM MM 2016, based on real world large scale dataset, and open evaluation system.

Thanks to the advance of deep learning algorithms, great progresses have been made in visual recognition in the past several years. But, there is still a big gap from these academic innovations and practical intelligent services, due to the lack of: (1) real world large scale data with better quality for training and evaluation. (2) public platform to conduct fair, efficient evaluations and make the recognition result reproducible and accessible.

To further motivate and challenge the academic and industrial research community, Microsoft is releasing MS-Celeb-1M, a large scale real world face image dataset to public, encouraging researchers to develop the best face recognition techniques to recognize one million people entities identified from Freebase. In its V1.0 version, the dataset contains 10M celebrity face images for the top 100K celebrities, which can be used to train and evaluate both face identification and verification algorithms.

Moreover, Microsoft Research has developed Prajna Hub, an open multimedia gateway, to convert latest algorithms into online services that can be accessed by anybody, from anywhere, and make the evaluation/test results repeatable and comparable.

This year we will focus on face recognition task. The contestants are asked to develop image recognition system based on, but not limited to, the datasets provided by the Challenge (as training data) to recognize 1M celebrities from their face images.

The 1M celebrities are obtained from Freebase based on their occurrence frequencies (popularities) on the web. Grounding the face recognition task to a knowledge base has many advantages. First, each people entity on Freebase is unique and clearly defined without disambiguation, making it possible to define such a large scale face recognition task. Second, each entity naturally has multiple properties (e.g. gender, date of birth, occupation), providing rich and valuable information for data collecting, cleaning, and multiple task learning.

The measurement set consists of 1000 celebrities sampled from the 1M celebrities, and for each celebrity we have manually labeled up to 20 images scrapped from commercial image search engines. But the identities of these 1000 celebrities will not be disclosed, so that the contestants cannot optimize just for these 1000 celebrities. To obtain high recognition recall and precision rates, the contestants will have to develop a recognizer to cover as many as possible celebrities, which will be of great value to help advance the state of the art in face recognition.

A contesting system is asked to produce 5 or less labels for a test image, ordered by confidence scores. Top one accuracies will be evaluated against a pre-labeled image dataset, which will be used during evaluation stage.

For DevSet1, the face images with big variations are used intentionally, in order to test the robustness your algorithm on complex situations such as pose/age/resolution/special effects/etc.. This track encourages participants to develop robust algorithms with good generalization capability.

For DevSet2, the test images are randomly selected, which are highly likely to be covered by the training data, if that entity is covered. In this way, the overall coverage of your algorithm, i.e., how many entities your system can recognize, among the 1M entities, can be evaluated. This track encourages participants to collect and use as many as training data as possible, and focus on the scalabilities.

The Challenge is a team-based contest. Each team can have one or more members, and an individual can be a member of multiple teams. No two teams, however, can have more than 1/2 shared members. The team membership must be finalized and submitted to the organizer prior to the Final Challenge starting date.

Fans

We understand that often times the photo ops you take with your favorite celebrity may be a once in a lifetime opportunity for you and we treat it as such. Our friendly courteous staff is always ready to help you at any convention we attend and after the convention via this website, our twitter and the Celeb Photo Ops facebook.

Your photos will be top quality 8 x 10 glossy photos printed on dye sublimation printers. The prints are of the highest quality and have a long archival life. They also accept signatures very well so you can even get your print autographed!

Event and Convention Promoters

Let the friendly professional Celeb Photo Ops crew handle photo ops at your next convention or event. You can visit our Booking page to find our more about us and what we can offer you, your guests and the fans at your convention or event.

If there's one place where celebrities will go to soft launch a relationship or sport a good 'fit, it's Sushi Park in Los Angeles. The "unassuming joint," as Google calls it, has served as a backdrop for campaigns and photo shoots and become a breeding ground for celebs to parade their flings and friendships.

It's the place where Kylie Jenner and Jordyn Woods were first spotted together after famously rekindling their friendship and where Bad Bunny and Kendall Jenner did their first group hangout. Justin and Hailey Bieber practically live there, and despite being based in Paris, Saint Laurent's creative director, Anthony Vaccarello, has also been quoted as a "regular."

Sushi Park is an omakase-only Japanese restaurant, meaning it only serves recommendations by the chef. The restaurant is called Sushi Park not because it's located in a parking lot-adjacent mall (though technically that's also true) but because of its owner and founder, Peter Park. It only opens Tuesday-Saturday for three hours a day, from 5:30 pm to 8:30 pm. Reviews on Yelp date as far back as 2008, but Sushi Park actually opened its doors in 2006.

The place has been open for almost two decades, but when did it become a celeb hotspot? Though the last couple of years have definitely seen an uptick in celeb spottings, it's been a go-to longer than you might think. A Yelp reviewer and frequent Sushi Park customer called the spot "a secret amongst A-list celebs" back in 2010.

According to Vanity Fair's In The Limelight podcast, Meghan Markle (back then, not a royal but a Suits star) recommended Sushi Park in her lifestyle blog The Tig back in the early 2010s, calling it her favorite sushi spot in L.A. and the place had been officially crowned as a "secret hangout" back in 2018. So, do we have Markle to owe for the celebrity craze? Maybe, but let's get into the food.

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