Deep reinforcement learning framework for characterizing video content
US10885341B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 25, 2018 |
| Grant date | Jan 5, 2021 |
| Priority date | — |
| Expiry date | Feb 2, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V40/174
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and systems for performing sequence level prediction of a video scene are described. Video information in a video scene is represented as a sequence of features depicted each frame. An environment state for each time step t corresponding to each frame is represented by the video information for time step t and predicted affective information from a previous time step t−1. An action A(t) as taken with an agent controlled by a machine learning algorithm for the frame at step t, wherein an output of the action A(t) represents affective label prediction for the frame at the time step t. A pool of predicted actions is transformed to a predicted affective history at a next time step t+1. The predictive affective history is included as part of the environment state for the next time step t+1. A reward R is generated on predicted actions up to the current time step t, by comparing them against corresponding annotated movie scene affective labels.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.