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.
For those who prefer a grittier tone and complex moral dilemmas.
Written by the legendary Naoki Urasawa, this story follows a group of friends who realize a cult leader is using a "Book of Prophecy" they wrote as children to destroy the world. 5. Sports (Spokon)
Set in a world where negative human emotions manifest as "Curses," Yuji Itadori joins a secret organization of Sorcerers to eliminate a powerful Curse named Ryomen Sukuna. The animation by MAPPA is industry-leading.
These series use sports as a vehicle for intense character growth and adrenaline-pumping drama.
For those who prefer a grittier tone and complex moral dilemmas.
Written by the legendary Naoki Urasawa, this story follows a group of friends who realize a cult leader is using a "Book of Prophecy" they wrote as children to destroy the world. 5. Sports (Spokon)
Set in a world where negative human emotions manifest as "Curses," Yuji Itadori joins a secret organization of Sorcerers to eliminate a powerful Curse named Ryomen Sukuna. The animation by MAPPA is industry-leading.
These series use sports as a vehicle for intense character growth and adrenaline-pumping drama.
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.
mp4hentaitonarinoienoanettesantheanim exclusive
3. Can we train on test data without labels (e.g. transductive)?
No.
For those who prefer a grittier tone and
4. Can we use semantic class label information?
Yes, for the supervised track.
mp4hentaitonarinoienoanettesantheanim exclusive
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.