Unified Video Retrieval and Grounding via Hierarchical Representations
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Solution Overview
Problem
Existing content-based video retrieval (CBVR) methods are limited in practical applications as they require long multi-sentence queries for video retrieval and cannot perform video grounding, making it difficult to identify relevant sections of retrieved videos.
Innovation Solution
A system and method that uses a learned hierarchical representation model for joint video retrieval and grounding, enabling retrieval and grounding with short word-based queries through a unified network architecture, using video-level representations to reduce computational and storage resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing CBVR methods use self-supervised or supervised representation learning to learn video representations, then video retrieval can be performed, but video grounding cannot be performed and long multi-sentence queries are required
Solution Approach 1:
The system segments the video into multiple clips and generates separate representations for each clip. This segmentation enables the model to process short queries effectively while maintaining the capability to perform both video retrieval and video grounding through clip-level analysis
Solution Approach 2:
The proposed model serves multiple functions: it can perform video retrieval using short queries, temporal video grounding to identify relevant clips, and spatial video grounding to locate objects within clips. This multi-functionality is achieved through a unified architecture that generates both video-level and clip-level representations
2Quantity of substance
If video-level representations are used instead of clip-level representations, then storage resources and computational resources are reduced, but the ability to perform detailed video grounding may be limited
Solution Approach 1:
The system generates hierarchical representations including both video-level representations (for efficient storage and retrieval) and clip-level representations (for precise grounding). This segmentation allows the model to balance between storage efficiency and grounding precision by using the appropriate representation level for each task
Solution Approach 2:
The model employs a nested representation structure where clip-level representations are nested within video-level representations. This hierarchical nesting allows the system to store compact video-level representations while maintaining detailed clip-level information when needed for grounding tasks
Data Source
AI summary
Methods and systems are described for performing video retrieval together with video grounding. A word-based query for a video is and encoded into a query representation using a trained query encoder. One or more similar video representations are identified, from a plurality of video representations that are similar to the query representation. Each similar video representation represents a respective relevant video. A grounding is generated for each relevant video by forward propagating each respective similar video representation together with the query representation through a trained grounding module. The relevant videos or identifiers of the relevant videos are outputted together with the grounding generated for each relevant video.