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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The rise of niche rural narratives on Indian OTT platforms, often exploring complex social dynamics and adult themes, reflects a shift away from traditional, sanitized television broadcasting [1]. The surge in smartphone usage has empowered creators to focus on decentralized stories that resonate with a broader audience, frequently employing a "rural noir" style to tackle previously taboo subjects [1]. As digital accessibility increases, these productions are reshaping the Indian entertainment landscape by focusing on localized storytelling and individual consumption [1].
The web series Gaon Ki Garmi, part of the popular Palang Tod anthology by Ullu Originals, has gained significant traction for its bold storytelling and rural setting. This adult drama series explores complex relationships and forbidden desires within a village backdrop. Plot Overview
The storyline primarily revolves around a city boy who visits his uncle and aunt in a remote village. He quickly notices a lack of intimacy and growing distance in his aunt's (Mami) marriage. The series follows the nephew's attempts to bridge this gap, leading to a series of steamy and controversial encounters. Later seasons, including Gaon Ki Garmi Season 2 and Season 3, introduce new characters like a young couple from the city whose arrival further complicates the existing relationship dynamics. Key Cast and Characters
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"Palang Tod" Gaon Ki Garmi 4: Part 7 (TV Episode 2023) - IMDb * Mahi Kaur. * Sofiya Shaikh. * Bhanu Suryam Thakur.
"Palang Tod" Gaon Ki Garmi 3: Part 1 (TV Episode 2023) - IMDb
Gaon Ki Garmi 3: Part 1 * Rafique. * Writer. Praveen Rajan. * Mahi Kaur. Bhanu Suryam Thakur. Anupam Gahoi. "Palang Tod" Gaon Ki Garmi: Part 1 (TV Episode 2021) - Plot
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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.