Integrating Simple Temporal Attention for Improved Video Summarization

Title

Integrating Simple Temporal Attention for Improved Video Summarization

Creator

Sarkar, Sarnali; Lamani, Manjunath Ramanna; Vinodha, D.

Description

Simple Temporal Attention (STA) in video summarization can improve deep learning model performance while tackling complexity and multi-view dependency problems. Many of the current models are too complex and dependent on multi-view setups to be scalable in single-camera settings. The suggested STA mechanism reduces model complexity without sacrificing accuracy, making it easier to recognize important moments in videos. To further increase the efficacy of summarization, a spatio-temporal mechanism is also introduced to capture crucial dynamics between video frames. The approach is evaluated on two benchmark datasets, UCF50 and TVSum, demonstrating significant improvements in model performance. This study provides a scalable solution for video summarization by highlighting the useful advantages of integrating STA for producing succinct and informative video summaries through a comparison of different deep learning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.

Source

Lecture Notes in Networks and Systems;Volume;1341 LNNS;pp.217-231

Date

01-01-2026

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Deep learning; Model complexity; Scalability; Simple temporal attention (STA); Single-camera environments; TVSum; UCF50; Video summarization are some examples of spatiotemporal mechanisms

Coverage

Sarkar S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Kumbalgodu, Karnataka, Bangalore, India; Lamani M.R., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Kumbalgodu, Karnataka, Bangalore, India; Vinodha D., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Kumbalgodu, Karnataka, Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-981965125-2;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-96-5126-9_16

https://www.scopus.com/pages/publications/105020795828?origin=resultslist