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.
While creating and sharing video content can be fun and engaging, it's also essential to approach the topic with sensitivity. For instance, some compilation videos may include reactions or moments that could be considered humorous by some but off-putting by others, sometimes referred to colloquially as the "puke" factor. It's crucial for creators to consider their audience and the potential impact of their content.
I recommend and ensuring your browser's security settings and antivirus software are up to date. If you were looking for a specific video or topic, please provide more context so I can help you find legitimate sources.
primarily link to creators focused on positive lifestyle content, such as: Art and Business Alina Blossom
While creating and sharing video content can be fun and engaging, it's also essential to approach the topic with sensitivity. For instance, some compilation videos may include reactions or moments that could be considered humorous by some but off-putting by others, sometimes referred to colloquially as the "puke" factor. It's crucial for creators to consider their audience and the potential impact of their content.
I recommend and ensuring your browser's security settings and antivirus software are up to date. If you were looking for a specific video or topic, please provide more context so I can help you find legitimate sources.
primarily link to creators focused on positive lifestyle content, such as: Art and Business Alina Blossom
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.
video+title+aleblossom+puke+compilation+cam+new
3. Can we train on test data without labels (e.g. transductive)?
No.
While creating and sharing video content can be
4. Can we use semantic class label information?
Yes, for the supervised track.
video+title+aleblossom+puke+compilation+cam+new
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.