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.
Some players use wrapper tools to translate old DirectX graphics calls into modern formats, preventing graphical glitches or crashes. The Timeless Appeal of Retro Puzzlers
These cheats effectively remove the restrictions imposed by the unregistered version. By utilizing these built-in developer tools, you can explore all the content of Digging Jim without ever needing a registration code.
Archive communities have occasionally reverse-engineered the original verification algorithm, creating "keygens" that allow users to type in any name and generate a functional registration code.
Let’s talk about the elephant in the mineshaft. Digging Jim is developed by a small, independent studio—likely just one or two people. The registration fee is not "corporate greed"; it’s how they pay for server costs, bug fixes, and new content.
Some players use wrapper tools to translate old DirectX graphics calls into modern formats, preventing graphical glitches or crashes. The Timeless Appeal of Retro Puzzlers
These cheats effectively remove the restrictions imposed by the unregistered version. By utilizing these built-in developer tools, you can explore all the content of Digging Jim without ever needing a registration code.
Archive communities have occasionally reverse-engineered the original verification algorithm, creating "keygens" that allow users to type in any name and generate a functional registration code.
Let’s talk about the elephant in the mineshaft. Digging Jim is developed by a small, independent studio—likely just one or two people. The registration fee is not "corporate greed"; it’s how they pay for server costs, bug fixes, and new content.
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.
Some players use wrapper tools to translate old
4. Can we use semantic class label information?
Yes, for the supervised track.
The registration fee is not "corporate greed"; it’s
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.