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.
Beyond the explicit content, the title Apoplanisi sti Santorini is rich with linguistic and cultural connotations.
Apoplanisi sti Santorini (Video 2012) - Full cast & crew - IMDb
To make the most of your visit to Sirina Apoplanisi Sti Santorini, here are a few tips and insights: Sirina.Apoplanisi.sti.Santorini.avi
Santorini is internationally famous for its volcanic caldera, steep cliffs, and dramatic sunsets. The film utilizes luxury villas, private pools, and scenic overlooks to create an aspirational, romantic, and highly stylized atmosphere before transitioning into adult content.
"Apoplanisi sti Santorini" (Seduction in Santorini) remains one of the most iconic titles from the Sirina archive. Between the white-washed alleys of Oia and the deep blue of the Aegean, it captured the ultimate Greek summer vibe of the early 2000s. Beyond the explicit content, the title Apoplanisi sti
Efforts to locate this file continue among Greek lost media collectors. If it ever resurfaces, it will likely be not on Netflix or YouTube, but on an old hard drive in a Thessaloniki basement, labeled simply: “Sirena – Santorini – do not delete.avi” .
Dimitris Sirinakis is not only a director and producer but also the owner of . This was the first Greek television channel dedicated exclusively to adult content, broadcasting via the OTE TV digital platform. This move legitimized the company's content within a legal broadcast framework. If it ever resurfaces, it will likely be
Her first morning in Oia the air tasted of sun-warmed stone and roasted coffee. White houses clung to cliffs like pages in a book, and every terrace held someone tracing the same horizon. Sirina unpacked on a balcony that faced the sea and hung a faded postcard of her mother on the nail above the kettle. Then she walked until the path narrowed to a stair and the island opened beneath her—blue spilling everywhere.
Who remembers the era of the .avi files? Before 4K streaming and high-speed fiber, Sirina was setting the bar for high-budget productions in some of the most beautiful locations in Greece.
The presence of at the end of the keyword is a strong indicator of early-to-mid 2010s internet culture.
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.