Last year, over 2 thousand teams participated in Kaggle’s Deepfake detection video classification challenge. For this task, contestants were provided 470 GB of high resolution video and required to submit a notebook which predicts whether each sample video file has been deepfaked with a 9 hour run-time limit.
Since most deepfake technology performs a faceswap, contestants concentrated around face detection and analysis. Beginning with face detection, contestants could develop an image classifier using the provided labels....
Intro In a previous post, we discussed scraping a movie poster image corpus with genre labels from imdb and learning image similarity models using tensorflow.
In this post, we extend this idea to recommend movie trailers based on audio-visual similarity.
Data We started by scraping IMDB for movie trailers and their genre tags as labels. Using Scrapy, it is easy to build a text file of video links to then download with youtube-dl....
We have seen applications in industries like retail, telemedicine, and robotics enabled by video analytics with machine learning. ML practitioners often leverage transfer learning with pretrained models to expedite development. Computer vision applications can benefit from using video analytics frameworks to facilitate faster iteration and experimentation.
NVIDIA’s TLT toolkit and the Deepstream SDK 5.0 have made it easy to experiment with various network architectures and quickly deploy them on a NVIDIA powered device for optimized inference....
Advances in methods to generate photorealistic but synthetic images have prompted concerns about abusing the technology to spread misinformation.
In response, major tech companies like Facebook, Amazon, and Microsoft partnered to sponsor a contest hosted by Kaggle to mobilize machine learning talent to tackle the challenge.
With $1 million in prizes and nearly half a terabyte of samples to train on, this contest requires the development of models that can be deployed to combat deepfakes....