Multimodal Pipeline Enhances Video Retrieval by Encoding Multiple Frames
A new system for video retrieval addresses the limitations of current methods that rely on single keyframes. Existing systems, especially in competitions, query individual images rather than entire clips, missing actions or events spanning multiple frames. This leads to inaccurate results because single frames lack sufficient information for higher-level abstraction. The proposed pipeline extracts multimodal data from multiple frames, enabling models to encode more abstract insights beyond object detection. By integrating the latest methodologies, the system improves the understanding of video content, allowing for more precise retrieval based on complex queries.
Key facts
- Current video retrieval systems focus on querying individual keyframes or images.
- Queries often describe actions or events over a series of frames.
- Single-frame analysis provides insufficient information for accurate results.
- Extracting embeddings only from images limits higher-level abstraction.
- The proposed system integrates the latest methodologies.
- The system introduces a novel pipeline that extracts multimodal data.
- The pipeline incorporates information from multiple frames within a video.
- The system enables models to abstract higher-level information.
Entities
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