The goal of the Kinetics dataset is to help the computer vision and machine learning communities advance models for video understanding. Given this large human action classification dataset, it may be possible to learn powerful video representations that transfer to different video tasks.
The Kinetics-700-2020 dataset will be used for this challenge. Kinetics-700-2020 is a large-scale, high-quality dataset of YouTube video URLs which include a diverse range of human focused actions. The aim of the Kinetics dataset is to help the machine learning community create more advanced models for video understanding. It is an approximate super-set of both Kinetics-400, released in 2017, Kinetics-600, released in 2018 and Kinetics-700, released in 2019.
The dataset consists of approximately 650,000 video clips, and covers 700 human action classes with at least 700 video clips for each action class. Each clip lasts around 10 seconds and is labeled with a single class. All of the clips have been through multiple rounds of human annotation, and each is taken from a unique YouTube video. The actions cover a broad range of classes including human-object interactions such as playing instruments, as well as human-human interactions such as shaking hands and hugging.
More information about how to download the Kinetics dataset is available here.
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The mid-20th century marked a massive shift. Filmmakers like Akira Kurosawa revolutionized global cinema with masterpieces like Seven Samurai . While the world has shifted toward mobile and
To fully understand Japanese entertainment, one must look at the unique social concepts that govern Japanese life and business.
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The Japanese entertainment industry is a multifaceted and dynamic sector that has been captivating audiences worldwide for decades. From the iconic anime and manga to the mesmerizing world of J-pop and J-rock, Japan's pop culture has become an integral part of modern entertainment. In this article, we'll explore the ins and outs of the Japanese entertainment industry, its history, key players, and cultural significance.
Japan's next frontier is post-human entertainment. Virtual YouTubers (VTubers) like Kizuna AI and Hololive's cast are anime avatars controlled by real voice actors. Their concerts sell out stadiums. Their fans form parasocial bonds with characters , not people. This is the logical conclusion of Azuma's "database consumption"—the person behind the avatar is irrelevant; only the moe (affection for fictional traits) elements matter.
1. Possible to use ImageNet checkpoints?
We allow finetuning from public ImageNet checkpoints for the supervised track -- but a link to the specific checkpoint should be provided with each submission.
2. Possible to use optical flow?
Flow can be used as long as not trained on external datasets, except if they are synthetic.
3. Can we train on test data without labels (e.g. transductive)?
No.
4. Can we use semantic class label information?
Yes, for the supervised track.
5. Will there be special tracks for methods using fewer FLOPs / small models or just RGB vs RGB+Audio in the self-supervised track?
We will ask participants to provide the total number of model parameters and the modalities used and plan to create special mentions for those doing well in each setting, but not specific tracks.