An Empirical Study of Excitation and Aggregation Design Adaptions in CLIP4Clip for Video-Text Retrieval
Xiaolun Jing, Genke Yang, Jian Chu·May 25, 2024
Summary
This study investigates the limitations of mean pooling in CLIP4Clip for video-text retrieval and proposes novel excitation-and-aggregation designs to enhance discriminative video representation. The authors address the issue by introducing an excitation module for recalibrating non-mutually-exclusive frame features and an aggregation module for learning exclusiveness. They apply these designs to different video representation types and test on MSR-VTT, ActivityNet, and DiDeMo datasets, achieving significant improvements over CLIP4Clip. The research highlights the effectiveness of the proposed modules in capturing temporal context and semantic relationships, leading to better retrieval performance. The study also discusses the importance of adapting to various paradigms and the need for future research in the field.
Introduction
Background
Overview of CLIP4Clip and its limitations in video-text retrieval
Objective
To address the limitations of mean pooling in CLIP4Clip
Propose excitation-and-aggregation designs for improved video representation
Aim for better temporal context and semantic relationship capture
Method
Data Collection
Selection of datasets: MSR-VTT, ActivityNet, DiDeMo
Data preprocessing and preprocessing challenges
Excitation Module
Design
Non-mutually-exclusive frame feature recalibration
Mechanisms for enhancing feature relevance
Implementation
Integration with CLIP4Clip architecture
Effect on frame-level feature recalibration
Aggregation Module
Learning Exclusiveness
Aggregation strategy for exclusive frame relationships
Temporal modeling and fusion techniques
Integration
Combining excitation and aggregation for enhanced video representation
Evaluation
Performance comparison with CLIP4Clip on benchmark datasets
Ablation studies on excitation and aggregation components
Results and Analysis
Improved retrieval accuracy on MSR-VTT, ActivityNet, and DiDeMo
Quantitative analysis of the proposed designs' impact
Case studies to demonstrate effectiveness in capturing context and relationships
Discussion
Limitations and future directions
Adaptability to different video-text retrieval paradigms
Importance of further research in the field
Conclusion
Summary of findings and contributions
Implications for video representation learning and video-text retrieval
Suggestions for future research in enhancing CLIP4Clip and other models.
Basic info
papers
computer vision and pattern recognition
multimedia
information retrieval
artificial intelligence
Advanced features
Insights
Which datasets are used for testing the effectiveness of the proposed excitation-and-aggregation designs?
What problem does the study focus on in the context of video-text retrieval?
What are the novel designs introduced by the authors to address the limitations of mean pooling in CLIP4Clip?
How do the proposed modules improve the performance of video representation compared to CLIP4Clip?