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 keyword "" refers to a specific installment of a popular Manipuri digital story series titled Eteima Thu Naba . This series is primarily shared across social media platforms like Facebook and YouTube , often following a serialized "part-by-part" format that has built a dedicated local following. Overview of the Eteima Thu Naba Series
The stories are set against a Manipuri backdrop, using local dialects and cultural nuances that resonate with the Meitei-speaking community. Finding "Part 12" on Facebook
The stories are usually told from a first-person perspective, often centering on the complex and sometimes illicit relationships between a younger male protagonist and an older female character ("Eteima" typically refers to an elder brother's wife or a sister-in-law figure). eteima thu naba part 12 facebook full
Being hosted on free platforms like Facebook makes it easy for mobile users in Manipur and the diaspora to access.
Readers often comment and share their theories on the plot, creating a virtual book club atmosphere around the "Wari". Way2News - Short News App - App Store The keyword "" refers to a specific installment
The series belongs to a genre of Manipuri digital literature often referred to as "Wari" (stories). While many stories in this category focus on folk tales or family dramas, Eteima Thu Naba specifically falls into adult-oriented fiction.
Across various parts, including Part 12, common narrative threads include: Finding "Part 12" on Facebook The stories are
The "Part 12" mentioned in the keyword indicates the long-running nature of the series, where readers follow the progression of the plot through frequent updates posted directly to Facebook pages. Plot Themes and Structure
The serialized format relies heavily on cliffhangers at the end of each part, driving search volume for subsequent parts like "Part 12".
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.