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 Lustery Legacy: Mike and Nina's Full Story"
"Exploring the lives and achievements of Mike and Nina Lustery, a dynamic duo making waves in their respective fields"
To develop a feature covering Mike and Nina Lustery, here are some potential ideas:
"The Lustery Legacy: Mike and Nina's Full Story"
"Exploring the lives and achievements of Mike and Nina Lustery, a dynamic duo making waves in their respective fields"
To develop a feature covering Mike and Nina Lustery, here are some potential ideas:
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.
mike nina lustery full
3. Can we train on test data without labels (e.g. transductive)?
No.
"The Lustery Legacy: Mike and Nina's Full Story"
4. Can we use semantic class label information?
Yes, for the supervised track.
here are some potential ideas:
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.