Snap Inc.

Chen Wang

Maksim Gusarov

Patrick Poirson

Zhenpeng Zhou

Huseyin Coskun

Oleg Tovstyi

Alireza Zareian

Joshua Moore

An input video item that includes a target visual augmentation is accessed. A machine learning model uses the input video item to generate an embedding. The embedding may comprise a vector representation of a visual effect of the target visual augmentation. The machine learning model is trained, in an unsupervised training phase, to minimize loss between training video representations generated within each of a plurality of training sets. Each training set comprises a plurality of different training video items that each include a predefined visual augmentation. Based on the generation of the embedding of the input video item, the target visual augmentation is mapped to an augmentation identifier.

Embeddings representing visual augmentations

Systems and methods are provided for clustering videos. The system accesses a plurality of content items, the plurality of content items comprising a first set of RGB video frames and a second set of optical flow frames corresponding to the first set of RGB video frames. The system processes the first set of RGB video frames by a first machine learning model to generate a first optimal assignment for the first set of RGB video frames, the first optimal assignment representing initial clustering of the first set of RGB video frames. The system generates an updated first optimal assignment for the first set of RGB video frames based on the first optimal assignment for the first set of RGB video frames and a second optimal assignment of the second set of optical flow frames, the second optimal assignment representing initial clustering of the second set of optical flow frames.

Clustering videos using a self-supervised DNN

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Great Neck, NY, United States of America

Chen Wang