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.
Modern partnerships increasingly place value on shared domestic chores and co-parenting.
This connectivity has also fueled a shift in social perspectives. Discussions around body positivity, financial independence, and late-age marriage are no longer taboo. The modern Indian woman is using her voice to redefine traditional "norms," choosing a life path that prioritizes her personal aspirations alongside her cultural duties. Conclusion
However, technology is the great liberator. The pressure cooker, microwave, and now the air fryer have reduced hours of toil. Moreover, the "Zomato/Swiggy" revolution (food delivery apps) has granted urban women a break from mandatory cooking. A distinct lifestyle shift is visible in metropolitan singles and working couples who often outsource cooking or embrace "one-pot meals."
Urban lifestyles have birthed "Indo-Western" fashion. Women frequently pair traditional kurtas with jeans, or style ethnic jackets over Western dresses. This style reflects the practical needs of a fast-paced urban lifestyle while honoring cultural roots. tamil aunty nude images
Cafes and global cuisines are transforming weekend leisure habits for young women. 4. Festivals, Rituals, and Spiritual Life
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Visible markers like the bindi (forehead dot), sindoor (vermilion in the hair parting), and mangalsutra (sacred necklace) carry deep cultural significance for married Hindu women, representing marital status and spiritual protection. Fashion, Clothing, and Identity The modern Indian woman is using her voice
The rise of co-working spaces and work-from-home culture post-COVID has been a game-changer, allowing women in smaller towns (Tier-2/3 cities) to participate in the gig economy without relocating.
For daily wear, comfort dictates fashion. Tunics paired with trousers or leggings (Kurtis) are the preferred uniform for university students and working professionals across cities.
Food and holistic health are central to the lifestyle of Indian women, acting as a bridge between ancestral wisdom and modern nutrition. and unprecedented change.
Despite significant progress, the journey of the Indian woman involves navigating deep-rooted societal challenges. The lifestyle of a woman in India is heavily influenced by the rural-urban divide, socioeconomic status, and regional mindsets.
Food is the language of love in India. The lifestyle of an Indian woman often revolves around the kitchen, but the approach has changed. While traditional slow-cooked meals are reserved for weekends, the weekday diet has become more global.
The life of an Indian woman is a story still being written—one of depth, complexity, and unprecedented change.
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.