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 path forward will be shaped by the growth of the creator economy, the continuous refinement of royalty systems in the music industry, and the increasing international co-productions that blend local stories with global appeal. Indonesia is not just an audience anymore; it is a protagonist, telling its own stories to the world through a dazzling array of screens, from the cinema to the smartphone.
| Creator | Subscribers (approx) | Niche | |---------|----------------------|-------| | | 30M+ | Vlogs, pranks, music, family | | Ria Ricis | 27M+ | Comedy, horror challenges, lifestyle | | Baim Wong | 20M+ | Celebrity vlogs, gaming, pranks | | Jess No Limit | 18M+ | Gaming (Mobile Legends), horror | | Raditya Dika | 12M+ | Stand-up, storytelling, satire | | Sisca Kohl | 10M+ | ASMR, eating challenges, mukbang | | Calon Sarjana | 8M+ | Mystery, horror, urban legends |
The path forward will be shaped by the growth of the creator economy, the continuous refinement of royalty systems in the music industry, and the increasing international co-productions that blend local stories with global appeal. Indonesia is not just an audience anymore; it is a protagonist, telling its own stories to the world through a dazzling array of screens, from the cinema to the smartphone.
| Creator | Subscribers (approx) | Niche | |---------|----------------------|-------| | | 30M+ | Vlogs, pranks, music, family | | Ria Ricis | 27M+ | Comedy, horror challenges, lifestyle | | Baim Wong | 20M+ | Celebrity vlogs, gaming, pranks | | Jess No Limit | 18M+ | Gaming (Mobile Legends), horror | | Raditya Dika | 12M+ | Stand-up, storytelling, satire | | Sisca Kohl | 10M+ | ASMR, eating challenges, mukbang | | Calon Sarjana | 8M+ | Mystery, horror, urban legends |
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.
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3. Can we train on test data without labels (e.g. transductive)?
No.
The path forward will be shaped by the
4. Can we use semantic class label information?
Yes, for the supervised track.
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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.