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.
Before configuring language settings, ensure that your setup meets the following criteria:
: Once you have downloaded the Language Pack-RUNE, extract the contents of the archive using a tool like 7-Zip or WinRAR.
This public link is valid for 7 days and shares a thread, including any personal information you added. This link or copies made by others cannot be deleted. If you share with third parties, their policies apply. Can’t copy the link right now. Try again later. Language pack. I can't change the audio language
Before configuring language settings, ensure that your setup meets the following criteria:
: Once you have downloaded the Language Pack-RUNE, extract the contents of the archive using a tool like 7-Zip or WinRAR.
This public link is valid for 7 days and shares a thread, including any personal information you added. This link or copies made by others cannot be deleted. If you share with third parties, their policies apply. Can’t copy the link right now. Try again later. Language pack. I can't change the audio language
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.
Before configuring language settings, ensure that your setup
4. Can we use semantic class label information?
Yes, for the supervised track.
Before configuring language settings
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.