Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. Bella Hadid is looking back on her time as a model. The 24-year-old star appeared on the cover of Vogue's September issue with several other young modeling stars and discussed what it was like for her when she kick-started her career. Hadid first found success when she was just 17 and said that she found it difficult to balance a public persona with her own personality.

Images tell powerful stories but cannot always be trusted. Matching images back to trusted sources (attribution) enables users to make a more informed judgment of the images they encounter online. We propose a robust image hashing algorithm to perform such matching. Our hash is sensitive to manipulation of subtle, salient visual details that can substantially change the story told by an image. Yet the hash is invariant to benign transformations (changes in quality, codecs, sizes, shapes, etc.) experienced by images during online redistribution. Our key contribution is OSCAR-Net (Object-centric Scene Graph Attention for Image Attribution Network); a robust image hashing model inspired by recent successes of Transformers in the visual domain. OSCAR-Net constructs a scene graph representation that attends to fine-grained changes of every object's visual appearance and their spatial relationships. The network is trained via contrastive learning on a dataset of original and manipulated images yielding a state of the art image hash for content fingerprinting that scales to millions of images.

We propose a unified model for three inter-related tasks: 1) to \textit{separate} individual sound sources from a mixed music audio, 2) to \textit{transcribe} each sound source to MIDI notes, and 3) to\textit{ synthesize} new pieces based on the timbre of separated sources. The model is inspired by the fact that when humans listen to music, our minds can not only separate the sounds of different instruments, but also at the same time perceive high-level representations such as score and timbre. To mirror such capability computationally, we designed a pitch-timbre disentanglement module based on a popular encoder-decoder neural architecture for source separation. The key inductive biases are vector-quantization for pitch representation and pitch-transformation invariant for timbre representation. In addition, we adopted a query-by-example method to achieve \textit{zero-shot} learning, i.e., the model is capable of doing source separation, transcription, and synthesis for \textit{unseen} instruments. The current design focuses on audio mixtures of two monophonic instruments. Experimental results show that our model outperforms existing multi-task baselines, and the transcribed score serves as a powerful auxiliary for separation tasks.

Answer your door from anywhere with the Nest Doorbell (battery) with an in intelligent camera. Whether it’s a person, package, animal, or vehicle, this …

Climate intelligence startups differ in their approaches, but, for the most part, use machine learning to analyze hordes of data, recognize patterns and …

Yesha will direct the artificial intelligence and machine learning program at IDSC, and continue serving as its innovation officer, a role she began in …

Amazon's cashier-less stores, automated packaging adopted by the company, and the launch of machine learning summer school were some of the …

It has been over 10 years since the Netflix Prize finished, and I was not expecting to write a blog post about it at this point. However, just in the past couple of weeks I have found myself talking about it extensively both in the context of a Twitter thread and discussion, as well as the shooting of an upcoming documentary series. Given that there seems to be continued interest as well as misunderstanding around the prize and its outcome, I thought it might be worth to "set the record straight" in a dedicated post. TLDR; While I am often misquoted as having said that the Netflix Prize was not useful for Netflix, that is only true about the grand prize winning entry. Along the way, Netflix got far more than our money's worth for the famous prize.

The game also gives players plenty to do while Ariana is singing. While at a real-life venue, you might only be able to stand up or sit down and dance in your seat, in Rift Tour, there's a way to emote, shoot guns, and keep riding through space and time to follow Ariana Grande's next song and other players. In the preview, my character, dressed as Ariana, watched a bigger version of Ariana. At one point, Rift Tour shifts from musical experience to boss battle, when players are pulled on a rollercoaster-like ride and made to shoot at one of "Fortnite's" largest monsters -- Storm King, who first appeared in a holiday 2018 event -- to attempt a high score.

Earlier this year, videos of Tom Cruise started popping up on TikTok of the actor doing some surprisingly un-Tom-Cruise-like stuff: goofing around in an upscale men's clothing store; showing off a coin trick; growling playfully during a short rendition of Dave Matthews Band's "Crash Into Me." In one video, he bites into a lollipop and is amazed to find gum in the center. "Mmmmm," he says to the camera. How come nobody ever told me there's bubblegum? The 10 videos, which were posted between February and June, featured an artificial intelligence-generated doppelganger meant to look and sound like him.