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 Kinetics-700-2020 dataset will be used for this challenge. Kinetics-700-2020 is a large-scale, high-quality dataset of YouTube video URLs which include a diverse range of human focused actions. The aim of the Kinetics dataset is to help the machine learning community create more advanced models for video understanding. It is an approximate super-set of both Kinetics-400, released in 2017, Kinetics-600, released in 2018 and Kinetics-700, released in 2019.
The dataset consists of approximately 650,000 video clips, and covers 700 human action classes with at least 700 video clips for each action class. Each clip lasts around 10 seconds and is labeled with a single class. All of the clips have been through multiple rounds of human annotation, and each is taken from a unique YouTube video. The actions cover a broad range of classes including human-object interactions such as playing instruments, as well as human-human interactions such as shaking hands and hugging.
More information about how to download the Kinetics dataset is available here.
If you head over to YouTube, TikTok, or any indie gaming forum, you’ll find hundreds of thousands of videos of people using "Windows 13." They are booting it up, customizing the taskbar, playing Minesweeper, and intentionally triggering the infamous Blue Screen of Death.
If you want to experience the vibe of Windows 13 without risking your PC, follow this safety protocol: windows 13 simulator
function bringToFront(winId) const winObj = windows.find(w => w.id === winId); if(winObj) winObj.zIndex = nextZ++; const el = document.getElementById(`win-$winId`); if(el) el.style.zIndex = winObj.zIndex; if(activeWindowId !== winId) document.querySelectorAll('.window').forEach(w => w.classList.remove('active')); el?.classList.add('active'); activeWindowId = winId; If you head over to YouTube, TikTok, or
Every Windows 13 simulator features an always-visible, often sarcastic AI assistant. Unlike Cortana or Copilot, this assistant (commonly named "Aura" or "Oracle") is . In one popular simulator, asking "What’s the weather?" results in the assistant drawing a random number between -40 and 120°F and displaying it with a generic cloud icon. This is a deliberate critique of cloud-dependent assistants and data harvesting. In one popular simulator, asking "What’s the weather
One of the most striking patterns in Windows 13 simulators is the . Instead, settings are modified by physically rearranging desktop icons, changing wallpaper hue, or speaking to a virtual paperclip named "Clip 2.0" (a dark parody of Clippy). This reflects real user frustration with nested settings menus—simulators propose a gestural, spatial, or conversational configuration model.
Enhanced Transparency effectsBuilding on "Mica" and "Acrylic" materials, Windows 13 concepts often push for total glass-morphism. Every window feels like a pane of frosted glass, with deep blur effects and vibrant gradients that react to the wallpaper. Why People Use Simulators
Beyond "eye candy," simulators are vital for . Just as a PC Building Simulator allows enthusiasts to practice hardware assembly without financial risk, a Windows 13 simulator allows developers to "stress test" new layout ideas. It provides a risk-free environment for students and hobbyists to explore complex system architectures before they are physically built. Why These Simulators Matter
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.
3. Can we train on test data without labels (e.g. transductive)?
No.
4. Can we use semantic class label information?
Yes, for the supervised track.
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.