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.
Through the lens of Japanese media, we can identify several key themes that underpin relationships and romantic storylines:
: Many of the deep romantic arcs are defined by this classic Japanese literary conflict. Players must balance what characters must do for their duty or family against what they want to do for love. 2. Key Romantic Storylines and Archetypes
It argues that in a broken world, the traditional markers of love—gifts, dates, confession letters—are luxuries. The only true proof of love is blood loss, shared secrets, and the willingness to turn your back on a monster because you trust your partner to shoot through you to kill it. Mad 22 Glory Quest Japanese Animal Dog Sex
Hana laughed—a genuine, unguarded sound. "That’s the most romantic thing anyone’s ever said in Mad Glory Quest ."
Japanese media is renowned for its romantic storylines, which often feature complex, dramatic, and passionate relationships. Some common tropes in Japanese romantic storylines include: Through the lens of Japanese media, we can
Izanagi Games ensures that the emotional state of your party directly influences your efficiency on the battlefield. The relationships you build in the narrative sections have tangible, mechanical consequences during tactical combat:
Players who may not enjoy:
Mad Glory Quest does not shy away from the darker aspects of intimacy and obsession. Reviews highlight that while the art style is "nice and cute," the narrative can get unexpectedly heavy. This duality is a hallmark of powerful storytelling. The romantic storylines here are not just about sweet moments; they explore the ugly sides of attachment, societal pressure, and the feeling of isolation even when you are with someone.
The game tracks every action you take during combat. Did you parry a strike aimed at your heroine? That is +1 Affection. Did you use your body as a shield against a grenade? That is +5. But crucially, did you trust the heroine to cover your blind spot while you executed a suicidal charge? That is +10 Key Romantic Storylines and Archetypes It argues that
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.