Invention Application
- Patent Title: Weakly Supervised Action Selection Learning in Video
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Application No.: US17716996Application Date: 2022-04-08
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Publication No.: US20220335718A1Publication Date: 2022-10-20
- Inventor: Junwei Ma , Satya Krishna Gorti , Maksims Volkovs , Guangwei Yu
- Applicant: The Toronto-Dominion Bank
- Applicant Address: CA Toronto
- Assignee: The Toronto-Dominion Bank
- Current Assignee: The Toronto-Dominion Bank
- Current Assignee Address: CA Toronto
- Main IPC: G06V20/40
- IPC: G06V20/40 ; G06V10/764 ; G06V10/774 ; G06V20/70 ; G06V10/82

Abstract:
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.
Public/Granted literature
- US12211274B2 Weakly supervised action selection learning in video Public/Granted day:2025-01-28
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