L'oreal

The Toronto-Dominion Bank

Elmoznino, Eric

Flament, Frederic Antoinin Raymond Serge

Jiang, Ruowei

Kezele, Irina

Ma, Junwei

Levinshtein, Alex

Despois, Julien

Aarabi, Parham

Ma, He

Perrot, Matthieu

Junwei Ma

Parham Aarabi

Maksims Volkovs

Irina Kezele

Alex Levinshtein

Guangwei Yu

Ruowei Jiang

Eric Elmoznino

Matthieu Perrot

Julien Despois

Frederic Antoinin Raymond Serge Flament

Satya Krishna Gorti

He Ma

Keyvan Golestan Irani

Noël Vouitsis

A text-video recommendation model determines relevance of a text to a video in a text-video pair (e.g., as a relevance score) with a text embedding and a text-conditioned video embedding. The text-conditioned video embedding is a representation of the video used for evaluating the relevance of the video to the text, where the representation itself is a function of the text it is evaluated for. As such, the input text may be used to weigh or attend to different frames of the video in determining the text-conditioned video embedding. The representation of the video may thus differ for different input texts for comparison. The text-conditioned video embedding may be determined in various ways, such as with a set of the most-similar frames to the input text (the top-k frames) or may be based on an attention function based on query, key, and value projections.

Text-conditioned video representation

A video localization system localizes actions in videos based on a classification model and an actionness model. The classification model is trained to make predictions of which segments of a video depict an action and to classify the actions in the segments. The actionness model predicts whether any action is occurring in each segment, rather than predicting a particular type of action. This reduces the likelihood that the video localization system over-relies on contextual information in localizing actions in video. Furthermore, the classification model and the actionness model are trained based on weakly-labeled data, thereby reducing the cost and time required to generate training data for the video localization system.

Weakly supervised action selection learning in video

There is shown and described a deep learning based system and method for skin diagnostics as well as testing metrics that show that such a deep learning based system outperforms human experts on the task of apparent skin diagnostics. Also shown and described is a system and method of monitoring a skin treatment regime using a deep learning based system and method for skin diagnostics.

Automatic image-based skin diagnostics using deep learning

Growing community of inventors

Toronto, Canada

Junwei Ma