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.
The narrative of "Vaaranam Aayiram" revolves around Surya (played by Suriya), a young man with a burning desire to become a filmmaker. His journey is not just about chasing his dreams but also about the various phases of life he encounters - from the innocence of childhood to the harsh realities of adulthood. The film is structured in a non-linear fashion, a testament to Selvaraghavan's storytelling prowess, as it seamlessly weaves together fragments of Surya's life, revealing his struggles, aspirations, and the unbreakable bond with his family, especially his grandfather (played by Parthiban) and father (played by Sathyaraj).
"Vaaranam Aayiram" has left a lasting impact on Tamil cinema, often cited as one of the best films in the history of Tamil film industry. It not only garnered critical acclaim but also performed well at the box office, further solidifying its place as a cult classic. The film's success can be attributed to its universal themes, strong narrative, and the ability to connect with audiences on an emotional level. tamilgun vaaranam aayiram
The performance of the cast, particularly Suriya, is noteworthy. Suriya brings to life the character of Surya with a vulnerability and authenticity that makes his journey relatable and engaging. The supporting cast, including Sathyaraj and Parthiban, add layers to the story with their impeccable performances. Selvaraghavan's direction deserves special mention for his bold attempt to explore complex human emotions and the challenges of following one's dreams, all while maintaining a delicate balance between melancholy and hope. The narrative of "Vaaranam Aayiram" revolves around Surya
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.