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.
Wetlands (2013) bukanlah film konsumsi massa yang ramah untuk semua orang. Film ini membutuhkan kedewasaan berpikir dan keterbukaan pikiran dari penontonnya. Namun, bagi Anda yang mengapresiasi sinema yang berani, jujur, dan keluar dari pakem Hollywood, film ini adalah sebuah mahakarya sinematik yang menyegarkan.
: The film explores coming-of-age, feminist rebellion, the impact of a dysfunctional family, and the processing of childhood trauma through radical bodily autonomy. Review and Content Warning
The search for reflects a dedicated audience’s desire for challenging, transgressive cinema with proper linguistic access. Wetlands is not a film for everyone—it is graphic, uncomfortable, and deliberately provocative. But for those who appreciate its raw honesty and dark humor, finding an exclusive high-quality version with accurate Indonesian subtitles is a true treasure hunt.
: Beberapa wilayah menyediakan film ini melalui layanan Prime Video atau saluran tambahan seperti Strand Releasing. nonton film wetlands 2013 sub indo exclusive
Directed by David Wnendt, this German adaptation of Charlotte Roche’s bestselling novel follows – a rebellious 18-year-old who uses her chaotic sexuality and lack of filters as weapons against her divorced parents. After a shaving accident (yes, there ) lands her in a proctology ward, she plots to reunite her parents by exploiting her own infected wound.
Beyond the surface-level rebellion, it explores feminist perspectives on the female body and the psychological effects of childhood neglect. Mature Content
Di postingan kali ini, saya akan mengupas tuntas mengapa film ini menjadi cult classic di kalangan penikmat film indie, tantangan mencari versi uncut dengan subtitle Indonesia, serta mengapa film ini penting di tengah gempuran sinema mainstream yang "terlalu bersih". Wetlands (2013) bukanlah film konsumsi massa yang ramah
: Di balik adegan-adegan yang memicu rasa mual (shock value), terdapat dialog-dialog melankolis tentang kesepian dan trauma masa kecil. Terjemahan yang baik membantu penonton menangkap sisi rapuh dari karakter Helen. Karakter Utama dan Performa Akting
Dalam dunia perfilman independen Jerman, hanya ada sedikit film yang mampu menciptakan gelombang kontroversi sekaligus pujian kritis seperti Wetlands (Feuchtgebiete) karya sutradara David Wnendt. Rilis pada tahun 2013, film ini langsung menjadi topik hangat di festival-festival film Eropa berkat penggambarannya yang mentah, jujur, dan sering kali menjijikkan tentang tubuh, seksualitas, dan trauma masa kecil.
Saat mencari link nonton, pastikan Anda memperhatikan hal-hal berikut: : The film explores coming-of-age, feminist rebellion, the
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Namun, alih-alih merasa menderita, Helen justru melihat masa rawat inapnya di rumah sakit sebagai sebuah kesempatan emas untuk menjalankan dua misi rahasia:
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.