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.
Websites that mimic social media logins to steal credentials.
Short for Ojek Online , referring to ride-hailing services.
This suggests a shift toward how this content is consumed as a form of "guilty pleasure" or underground entertainment that bypasses mainstream media filters. The "Prank" as Narrative Websites that mimic social media logins to steal credentials
At the heart of the "ojol" (online ojek/transportation) keyword is a long-standing trend in Indonesian social media. Content creators often use delivery drivers as participants in "pranks." While some of these are heartwarming—such as surprising a driver with a large tip or a new bike—others lean into more controversial territory. The lifestyle and entertainment aspect of these videos often plays on the "unexpected encounter" trope, which draws millions of views across platforms like YouTube and TikTok. Deciphering the Jargon
The string of keywords provided includes several slang terms and codes: The "Prank" as Narrative At the heart of
Much of this content is shared without the consent of the individuals involved, raising serious ethical questions within the entertainment industry. Conclusion: The Evolution of Viral Content
These are frequently used as "tags" or "codes" within specific digital communities to categorize content, often related to mature entertainment or viral "full" videos. Deciphering the Jargon The string of keywords provided
In the realm of digital lifestyle, "prank" content has evolved. It is no longer just about a simple joke; it is often scripted "entertainment" designed to mimic reality. The term "gak puas lanjut" (not satisfied, continuing) implies a multi-part narrative structure, a common tactic used by creators to keep viewers clicking through a series of videos or "full" versions of a story. Consumption Trends and Online Safety
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.