Prompting Visual-Language Models for Efficient Video Understanding

Chen Ju; Tengda Han; Kunhao Zheng; Ya Zhang; Weidi Xie

doi:10.1007/978-3-031-19833-5_7

ScienceGate Book Chapters

BOOK-CHAPTER

Prompting Visual-Language Models for Efficient Video Understanding

Chen Ju Tengda Han Kunhao Zheng Ya Zhang Weidi Xie

Year: 2022 Lecture notes in computer science Pages: 105-124 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-031-19833-5_7

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Artificial intelligence Transformer Shot (pellet) Language model Set (abstract data type) Machine learning Natural language processing Programming language

Metrics

314

Cited By

51.63

FWCI (Field Weighted Citation Impact)

Refs

1.00

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Multimodal Machine Learning Applications

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Human Pose and Action Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Domain Adaptation and Few-Shot Learning

Physical Sciences → Computer Science → Artificial Intelligence

Prompting Visual-Language Models for Efficient Video Understanding

Metrics

Citation History

Topics

Related Documents

BiEfficient: Bidirectionally Prompting Vision-Language Models for Parameter-Efficient Video Recognition

Understanding AI: Large Language Models, and Prompting

LongVLM: Efficient Long Video Understanding via Large Language Models

Text-Visual Prompting for Efficient 2D Temporal Video Grounding

Metacognitive Prompting Improves Understanding in Large Language Models