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.
He floats down a river to escape the scent, eventually finding a group of drifters led by Granger. These people are "living books"—each has memorized a classic work of literature. They accept Montag. Meanwhile, the city is destroyed by a nuclear war. Montag and the group resolve to return one day to help rebuild civilization, remembering the wisdom of the books they carry in their heads.
El protagonista, , es un bombero que disfruta de su trabajo hasta que conoce a Clarisse McClellan , una vecina adolescente de 17 años. Clarisse es diferente: le gusta caminar por la lluvia, hablar con la gente y hacer preguntas como "¿Eres feliz?" . Esta simple pregunta empieza a perturbar a Montag. Fahrenheit 451 Resumen El Rincon Del Vago BEST
¿Necesitas algo más para completar tu tarea? Te puedo ayudar con: Una comunes para exámenes. He floats down a river to escape the
Montag intenta comprender lo que leen, pero se siente frustrado. Busca ayuda de un viejo profesor de inglés llamado Faber. Juntos planean plantar libros en las casas de otros bomberos para desacreditar al sistema. Sin embargo, Mildred denuncia a Montag. Beatty obliga a Montag a quemar su propia casa. En un giro violento, Montag mata a Beatty con el lanzallamas y escapa. Es perseguido por un “Mecano-perro” y por la televisión en vivo. Finalmente, huye al campo y se une a un grupo de intelectuales nómadas que preservan los libros memorizándolos. La novela termina con la explosión nuclear de la ciudad, y Montag lidera a los hombres sobrevivientes hacia la reconstrucción de una sociedad basada en el conocimiento. Meanwhile, the city is destroyed by a nuclear war
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.