Weakly-supervised action localization by sparse temporal pooling network
US11881022B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 10, 2023 |
| Grant date | Jan 23, 2024 |
| Priority date | — |
| Expiry date | Mar 10, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/44
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods for a weakly supervised action localization model are provided. Example models according to example aspects of the present disclosure can localize and/or classify actions in untrimmed videos using machine-learned models, such as convolutional neural networks. The example models can predict temporal intervals of human actions given video-level class labels with no requirement of temporal localization information of actions. The example models can recognize actions and identify a sparse set of keyframes associated with actions through adaptive temporal pooling of video frames, wherein the loss function of the model is composed of a classification error and a sparsity of frame selection. Following action recognition with sparse keyframe attention, temporal proposals for action can be extracted using temporal class activation mappings, and final time intervals can be estimated corresponding to target actions.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.