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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Ultimately, the entertainment landscape of 2021 proved that audiences crave high-concept realism mixed with diverse representation. Whether analyzing production house strategies, regional cinematic triumphs, or digital brand building, the year established a decentralized, democratic template for future global media. To help tailor this analysis further, please share:
The announcement generated massive social media traction. Why? Because Qala represented a continuation of the “Bulbbul aesthetic”—melancholic beauty, haunting music, and deep psychological trauma. Anushka’s production house was no longer just making films; they were building a recognizable brand: arthouse sensibilities with commercial packaging. anushka xxx 2021
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Throughout 2021, her Instagram following skyrocketed, making her one of the most followed Indian TV actresses on the platform. She became a sought-after face for international brands, blending fashion, lifestyle, and acting. Creative Evolution : Towards the end of 2021, reports surfaced
Anushka’s popularity in 2021 was heavily mediated through her social media channels, where she maintained a "relatable celebrity" brand.
Anushka Sen was one of the most active young stars in 2021, transitioning from child acting to mainstream reality and digital media. Fear Factor: Khatron Ke Khiladi 11 To help tailor this analysis further, please share:
also drew significant public attention for her commentary on digital media, particularly regarding the toxic nature of social interactions.
No discussion of Anushka Sharma’s 2021 media presence is complete without acknowledging the "VK18" factor. As the wife of Indian cricket captain Virat Kohli, Anushka existed at the intersection of two massive Indian obsessions: cinema and cricket. In 2021, with Kohli stepping down as T20 captain and navigating team changes, the media’s gaze on the couple intensified.
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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.