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.
Navigating the Streets of Los Santos: A Deep Dive into GTA 5 (Grand Theft Auto V) Updates 1.64 to 1.66
The world of Grand Theft Auto V and its ever-evolving multiplayer counterpart, GTA Online , continues to dominate the gaming landscape over a decade after its initial release. This longevity is fueled by Rockstar Games' commitment to consistent updates, bug fixes, and "extra quality" refinements. For players tracking the evolution of the game, the transition from through Update 1.66 represents a pivotal era of content expansion and technical stabilization.
Rockstar used this period to fine-tune the payouts for various missions, ensuring that the "Los Santos Drug Wars" content remained competitive with older heists. The Refinement: Update 1.66 and Beyond gta 5 grand theft auto v update 164 166 extra quality
Players met Dax and the "Fooliganz," embarking on the "First Dose" series of missions. These missions brought back the psychedelic, high-octane storytelling Rockstar is known for.
The journey from was a transformative period for Grand Theft Auto V . It successfully balanced the chaotic fun of the Los Santos Drug Wars with the necessary technical "extra quality" required to keep a massive community thriving. Whether you are returning for the story missions or the high-fidelity vehicle upgrades, these versions represent GTA V at its most refined. Navigating the Streets of Los Santos: A Deep
Five new story missions concluded the battle against Dr. Isiah Friedlander, providing a cinematic end to the Fooliganz storyline.
By the time rolled around in early 2023, the focus shifted toward "The Last Dose" (the conclusion of the Drug Wars saga) and significant technical overhauls. Rockstar used this period to fine-tune the payouts
While 1.64 was about content, acted as a crucial stabilization patch. In the world of live-service gaming, "extra quality" often means fixing the things that break when new content is added.
This update focused heavily on PC security, addressing vulnerabilities that allowed malicious actors to interfere with other players' accounts.
As we look toward the future of the franchise, the lessons learned in stability and content delivery during the 1.6x update cycle continue to set the standard for what players expect from the streets of Los Santos.
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.