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.
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Japanese cinema holds a paradoxical position: it is simultaneously revered as high art and mass-produced as commercial pop.
The Golden Age Legacy: The world first fell in love with Japanese entertainment through directors like Akira Kurosawa (Seven Samurai), Yasujirō Ozu (Tokyo Story), and Kenji Mizoguchi (Ugetsu). These directors introduced Western audiences to wabi-sabi (the beauty of imperfection) and mono no aware (the bittersweet awareness of transience). Their influence on Hollywood is immeasurable—from George Lucas borrowing the "hidden fortresse" structure for Star Wars to Quentin Tarantino’s visual homages in Kill Bill. jav attackers slave island verified
The J-Horror Wave: In the late 1990s and early 2000s, Japanese horror (Ringu, Ju-On) revolutionized the genre. Eschewing the slasher tropes of the West, J-Horror relied on atmosphere, psychological dread, and folklore ghosts (yurei) with long, black hair crawling out of wells. This proved that Japanese storytelling could terrify the world without a single chainsaw.
The Anime Ascendancy: Today, anime is the undisputed king of Japanese cinema. Makoto Shinkai’s Your Name. grossed over $380 million worldwide, outselling traditional live-action blockbusters. The success of Demon Slayer: Mugen Train (2020) as the highest-grossing film in Japanese history (surpassing Spirited Away) demonstrated that anime is no longer a niche genre—it is mainstream entertainment. Japanese animation studios have mastered a hybrid model: low-cost TV production for weekly serials (e.g., One Piece) combined with high-budget, cinematic event films. Not every “JAV Attackers Slave Island” is legit
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The Ministry of Foreign Affairs officially recognized kawaii (cuteness) as a diplomatic tool. Hello Kitty was appointed as a tourism ambassador. Anime characters grace Japanese passports (the Japan Passport featuring The Tale of the Bamboo Cutter). This blend of commerce and statecraft has made Japanese pop culture more palatable globally than its hard-power neighbors (China and South Korea), though South Korea’s K-Pop wave has recently overtaken J-Pop in global relevance. in high-end anime and manga
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Japan historically embraces automation but reveres human craft (takumi). AI-generated art is already appearing in mobile game backgrounds and light novel illustrations. Yet, in high-end anime and manga, AI is rejected. The hand-drawn line art of a master like Hayao Miyazaki—who called AI art "an insult to life itself"—represents a Luddite bulwark against algorithmic content.
For decades, Japanese media was locked behind region-coded DVDs and geoblocked streaming. Netflix’s $2 billion investment in Japanese content (including live-action One Piece, City Hunter, and Yu Yu Hakusho) has blown the doors open. However, this creates friction: Japanese producers must now conform to "global visual standards" (faster pacing, less cultural exposition), risking the erasure of the very idiosyncrasies that made Japan unique.
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.