Enterprise AI Analysis
Unified Interactive Multimodal Moment Retrieval via Cascaded Embedding-Reranking and Temporal-Aware Score Fusion
This paper introduces a novel multimodal moment retrieval system addressing key challenges in video content analysis. It features a cascaded dual-embedding pipeline (BEiT-3, SigLIP, BLIP-2) for balanced recall and precision, a temporal-aware scoring mechanism with exponential decay and beam search for coherent event sequences, and an Agent-guided query decomposition (GPT-40) for adaptive modality fusion. The system handles ambiguous queries, ensures temporal coherence, and dynamically adjusts fusion strategies, advancing interactive moment search capabilities. Qualitative results confirm its effectiveness in handling complex retrieval scenarios.
Key Impact Metrics
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Overall Retrieval Score Achieved
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Key Innovations in Multimodal Retrieval
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Cascaded Retrieval
Temporal Reasoning
Adaptive Fusion
The system employs a dual-stage retrieval pipeline. Initial broad candidate retrieval uses efficient dual encoders like BEiT-3 and SigLIP. This stage optimizes for high recall. Subsequently, a more precise, but computationally intensive, cross-encoder (BLIP-2's Image-Text Matching head) reranks the top candidates, balancing recall and precision effectively.
A temporal-aware scoring mechanism is introduced, applying exponential decay penalties to large temporal gaps. This encourages the formation of coherent event sequences via beam search, rather than retrieving isolated frames. This method handles varying event durations and prevents unrealistic temporal disjointedness, reflecting human perception of event coherence.
Agent-guided query decomposition (GPT-4o) automatically interprets ambiguous natural-language queries. It decomposes them into modality-specific sub-queries (visual, OCR, ASR) and dynamically assigns weights for adaptive score fusion. This eliminates the need for manual modality selection and robustly handles cross-modal noise.
76.4%
Overall Retrieval Score Achieved
Unified Retrieval Process Flow
User Query Input
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GPT-4o Decomposition & Weighting
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Parallel Modality Search (Visual/OCR/ASR)
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Adaptive Score Fusion (Min-Max Norm)
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Beam Search for Temporal Coherence
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BLIP-2 Reranking & Validation
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Ranked Relevant Moments
| Feature | Traditional Systems | Our System |
|---|---|---|
| Modality Selection | Manual/Fixed Fusion |
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| Temporal Modeling | Fixed Windows/Weak Gaps |
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| Retrieval Precision | Single-Stage (Lower Precision) |
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Case Study: Ambiguous Query Resolution
A user query like 'Find a cooking scene where the clock shows 10 minutes' demonstrates the system's ability to interpret and fuse information from multiple modalities.
- GPT-4o decomposes the query into visual ('cooking scene'), OCR ('clock shows 10 minutes'), and ASR (null) sub-queries.
- Visual search leverages BEiT-3/SigLIP. OCR search targets on-screen text.
- Adaptive fusion assigns higher weight to OCR, accurately prioritizing frames with '10 minutes' on a clock, even amidst visual similarities.
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Your Implementation Roadmap
A phased approach to integrate advanced AI into your enterprise.
Phase 1: Discovery & Strategy Alignment
Conduct detailed needs assessment, define specific retrieval goals, and align AI strategy with business objectives. Establish key performance indicators (KPIs).
Phase 2: Data Preparation & Indexing
Prepare video datasets, integrate with existing systems, and initiate the offline indexing pipeline for multimodal data extraction and embedding generation.
Phase 3: System Integration & Customization
Integrate the retrieval system into your enterprise environment. Customize query decomposition and fusion parameters based on initial data insights and user feedback.
Phase 4: Pilot Deployment & User Feedback
Launch a pilot program with a select user group. Gather feedback, monitor performance, and iterate on system configurations for optimal results.
Phase 5: Full Rollout & Continuous Optimization
Full-scale deployment across the organization. Establish continuous learning loops for model refinement and adaptive fusion strategy evolution.
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