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Zero-Shot Video Grounding With Pseudo Query Lookup and Verification
Summary
This study introduces a new zero-shot video grounding (ZS-VG) framework, Lookup-and-Verification (LoVe), to improve video understanding. LoVe efficiently identifies video moments using natural language queries without extensive manual annotation.
Area of Science:
- Computer Science
- Artificial Intelligence
- Machine Learning
Background:
- Video grounding, identifying video segments from text queries, is crucial for video understanding.
- Fully supervised methods require extensive data, hindering scalability.
- Existing zero-shot video grounding (ZS-VG) methods struggle with diverse categories and contextual dynamics.
Purpose of the Study:
- To address limitations in current ZS-VG approaches.
- To develop a novel framework for efficient and accurate zero-shot video grounding.
- To improve the recognition of diverse categories and contextual interactions in videos.
Main Methods:
- Introduced a two-stage zero-shot video grounding (ZS-VG) framework named Lookup-and-Verification (LoVe).
- Treated pseudo-query generation as a video-to-concept retrieval problem.
- Implemented a verification process to ensure retrieved concepts align with video content.
Main Results:
- The LoVe framework demonstrated effectiveness in zero-shot video grounding.
- Achieved strong performance on benchmark datasets like Charades-STA, ActivityNet-Captions, and DiDeMo.
- Showcased improved ability to recognize diverse categories and capture video dynamics.
Conclusions:
- The LoVe framework offers a promising solution for zero-shot video grounding.
- The proposed method enhances video understanding by overcoming limitations of existing approaches.
- LoVe provides a scalable and effective approach for video moment retrieval.
