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.
. By transplanting these tropes into a London-based theatrical troupe, the film creates a "stage within a stage" where the bumbling protagonists—Bunty (Akshay Kumar), Babla (Govinda), and Champak (Paresh Rawal)—cannot distinguish between scripted drama and real-world peril. 2. The Mechanics of Situational Humor The film’s first act serves as a masterclass in situational comedy, relying on: The Power Trio: The chemistry between Kumar, Rawal, and Govinda serves as the film's anchor. While Kumar plays the mischievous "Bunty," Govinda's "Babla" provides a foil through impeccable comic timing. Misunderstanding as Catalyst: Much of the conflict arises from linguistic and situational errors, such as the accidental switching of suitcases containing heroin, which transforms simple actors into unwitting fugitives. 3. Tonal Dichotomy: Comedy vs. Whodunit A recurring critique of the film is its "split personality." The first half focuses on the search for a lead actress (Aditi/Munni, played by Lara Dutta), while the second half descends into a dark murder mystery involving drug cartels and the London Police. The Climax: The final sequence, set atop a clock tower and involving a fire brigade rescue gone wrong, is a literalization of the title’s "mad rush." It utilizes slapstick physics—inspired by
What starts as a lighthearted comedy quickly spirals into a dark thriller as the troupe gets unwittingly entangled with a drug cartel, a mysterious woman named Munni (Lara Dutta), and a complex murder investigation.
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The story follows a struggling theater troupe led by (Paresh Rawal), who travels to London for a high-stakes show. Trouble begins when the group's lead actors, Bunty (Akshay Kumar) and Babla (Govinda), compete to find a replacement heroine after their original lead quits.
Released in December 2006, is a cornerstone of mid-2000s Bollywood comedy. Directed by Priyadarshan , the film is a high-octane mix of slapstick humor, chaotic "run-around" sequences, and a gripping murder mystery. Nearly two decades later, it remains a cult classic, dominating social media through viral memes and digital reruns. Movie Overview: Plot and Highlights
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.