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.
AVS-MUSEUM-DPHN-142 is a Japanese production house and entertainment company that specializes in creating adult-oriented content, including uncensored drama series, films, and other forms of entertainment. The company's name might seem cryptic, but it's a combination of various elements that reflect its brand identity. "AVS" likely stands for "Adult Video Series" or "Audio-Visual Series," while "MUSEUM" suggests a collection of artistic and creative works. "DPHN-142" appears to be a unique identifier or a code that represents a specific product or series.
A key distinction of the line is its dedication to the "Drama" genre. While much of the adult industry pivots toward gonzo or reality-style filming, titles like DPHN-142 maintain a strong focus on storytelling.
AVS-MUSEUM-DPHN-142 is a Japanese production house and entertainment company that specializes in creating adult-oriented content, including uncensored drama series, films, and other forms of entertainment. The company's name might seem cryptic, but it's a combination of various elements that reflect its brand identity. "AVS" likely stands for "Adult Video Series" or "Audio-Visual Series," while "MUSEUM" suggests a collection of artistic and creative works. "DPHN-142" appears to be a unique identifier or a code that represents a specific product or series.
A key distinction of the line is its dedication to the "Drama" genre. While much of the adult industry pivots toward gonzo or reality-style filming, titles like DPHN-142 maintain a strong focus on storytelling.
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.
-AVS-MUSEUM-DPHN-142 Uncensored Part2
3. Can we train on test data without labels (e.g. transductive)?
No.
including uncensored drama series
4. Can we use semantic class label information?
Yes, for the supervised track.
-AVS-MUSEUM-DPHN-142 Uncensored Part2
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.