Enterprise AI Analysis
MADTempo: Multi-Event Temporal Video Retrieval with Query Augmentation
The rapid expansion of video content across online platforms has intensified the need for retrieval systems capable of understanding not only isolated visual moments but also the temporal structure of complex events. Existing approaches often fall short in modeling temporal dependencies across multiple events and in handling queries that reference unseen or rare visual concepts. To address these challenges, we introduce MADTempo, a video retrieval framework developed by our team, AIO Trình, that unifies temporal search with web-scale visual grounding. Our temporal search mechanism captures event-level continuity by aggregating similarity scores across sequential video segments, enabling coherent retrieval of multi-event queries. Complementarily, a Google Image Search-based fallback module expands query representations with external web imagery, effectively bridging gaps in pretrained visual embeddings and improving robustness against out-of-distribution (OOD) queries. Together, these components advance the temporal reasoning and generalization capabilities of modern video retrieval systems, paving the way for more semantically aware and adaptive retrieval across large-scale video corpora.
Executive Impact at a Glance
MADTempo delivers advanced video retrieval capabilities, crucial for enterprises managing vast video archives and requiring precise, context-aware content access.
75.4%
Official Score, Ho Chi Minh City AI Challenge 2025
6
Key Authors Driving Innovation
3
Core Contributions to Video Retrieval
4
Distinct Retrieval Task Categories Excelled In
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Overview
System Capabilities
Core Algorithm (TRAKE)
Technical Details
Performance & Results
Overview of MADTempo
MADTempo is an interactive video retrieval system designed to overcome the limitations of existing approaches in understanding temporal structures of complex events and handling out-of-distribution (OOD) queries. It unifies temporal search with web-scale visual grounding, offering robust and semantically aware retrieval across large-scale video corpora.
Key aspects include:
Multi-Event Temporal Search: Captures event-level continuity by aggregating similarity across sequential video segments, enabling coherent retrieval for complex queries.
Web-Scale Visual Grounding: A Google Image Search-based fallback module augments query representations with external web imagery, bridging gaps in visual embeddings for OOD queries.
Scalable Architecture: Utilizes Milvus for high-speed vector search and MongoDB for comprehensive metadata management.
Key Takeaways & Keywords
This research highlights critical advancements for modern video management systems:
Multimodal Video Retrieval: Integrating various data types (visual, text, audio) for richer understanding.
Temporal Event Search: Specifically designed to find sequences of events, not just isolated moments.
Multi-Event Query: Capability to process complex queries spanning multiple actions or occurrences.
CLIP-based Embedding: Leveraging powerful vision-language models for semantic alignment.
Semantic Video Understanding: Moving beyond mere object recognition to contextual comprehension.
Temporal Reasoning: The ability to understand and process the order and duration of events.
Interactive Retrieval Interface: User-friendly design for efficient query formulation and result exploration.
Multimodal Representation Learning: How different data modalities are jointly learned to create robust features.
Large-Scale Video Corpus: Designed for efficiency and scalability when dealing with massive video datasets.
MADTempo vs. Traditional Video Retrieval
MADTempo significantly advances video retrieval by addressing key limitations of traditional systems, offering superior temporal reasoning, OOD query handling, scalability, and comprehensive multimodal integration.
| Feature | Traditional Systems | MADTempo |
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| Multi-Event Temporal Reasoning |
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| Out-of-Distribution (OOD) Query Handling |
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| Scalability & Efficiency |
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| Multimodal Integration |
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TRAKE: Multi-Event Temporal Search Framework
TRAKE (Temporal Retrieval for A Keyframe Event) is MADTempo's core algorithm for multi-event temporal video retrieval. It decomposes a natural language query into context and an ordered sequence of events. It uses CLIP-LAION embeddings for event retrieval, validates temporal coherence with MongoDB metadata, and employs structured temporal reasoning (e.g., 'A after B') to identify event sequences consistent with user-specified constraints. A beam search strategy ensures strict temporal ordering and maximizes event alignment scores.
Data Preprocessing and Feature Extraction Pipeline
The preprocessing pipeline transforms raw video into a structured multimodal database. Keyframes are extracted, deduplicated, and enriched with embeddings and metadata (objects, text, captions, audio transcripts) before being stored in Milvus and MongoDB, respectively.
Enterprise Process Flow
RAW Videos
→
Scene Extraction (TransNetV2)
→
Keyframes
→
Image Embedding (CLIP-Laion2B)
→
Keyframe Deduplication
→
Object Detection (YOLOv8)
→
OCR (Vintern)
→
Image Captioning (Qwen2.5VL)
→
Speech Detection (PhoWhisper)
→
Milvus Vector Database
→
MongoDB Metadata Database
Context-Expanded Image Query (Google Image Search)
To enhance query representations for rare or ambiguous concepts, MADTempo integrates a Google Image Search-based fallback. Users can input a text query, retrieve top-k images, select a representative one, and its CLIP-LAION embedding then augments the original query context. This boosts semantic robustness for out-of-distribution (OOD) queries, trading some latency for improved accuracy.
Performance in Ho Chi Minh City AI Challenge 2025
The MADTempo system, developed by team AIO Trình, achieved a strong official score of 75.4 in the preliminary round of the Ho Chi Minh City AI Challenge 2025, demonstrating its effectiveness in real-world video retrieval scenarios. This performance highlights the robustness of its multi-event temporal search and OOD query handling.
75.4
Official Score in Ho Chi Minh City AI Challenge 2025 Preliminary Round
Detailed Performance Breakdown (Final Round)
In the subsequent Final Round, the system was evaluated across four distinct retrieval task categories, ultimately securing a Very good overall performance ranking. The detailed performance breakdown for the Final Round is as follows:
TKIS tasks (Text-to-Keyframe): Good
VKIS tasks (Visual-to-Keyframe): Very good
TRAKE tasks (Temporal Retrieval): Excellent
QA tasks (Query Answering): Good
This demonstrates MADTempo's robust multi-event temporal search algorithm and the successful integration of its Google Image Search-based fallback mechanism for handling challenging out-of-distribution (OOD) visual queries.
Calculate Your Potential AI ROI
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Estimated Annual Savings
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Your AI Implementation Roadmap
A phased approach ensures seamless integration and maximum impact for your enterprise.
Phase 1: Discovery & Strategy
Conduct a deep dive into your current video management workflows, identifying key challenges and opportunities for AI integration. Define clear objectives and a tailored strategy for MADTempo deployment.
Phase 2: Data Preparation & Model Customization
Prepare and structure your enterprise's video data. Customize MADTempo's models and integrate auxiliary modalities (OCR, ASR, object detection) to optimize performance for your specific content and query patterns.
Phase 3: System Integration & Testing
Integrate MADTempo into your existing infrastructure, ensuring seamless data flow with Milvus and MongoDB. Rigorous testing and validation are performed to verify system accuracy, scalability, and robustness.
Phase 4: Deployment & Optimization
Full deployment of MADTempo into production environments. Continuous monitoring, performance tuning, and user feedback incorporation for ongoing optimization and feature enhancements.
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