U-CESE: Unified Clip-based Event Search Engine for AI Challenge HCMC 2025

• URL: http://arxiv.org/abs/2605.23274v1
• Date: Fri, 22 May 2026 06:28:41 GMT
• Title: U-CESE: Unified Clip-based Event Search Engine for AI Challenge HCMC 2025
• Abstract summary: U-CESE is a Unified Clip-based Event Search Engine for multimodal event retrieval across diverse video sources.<n>A core component is the Unified Clipping Algorithm, which merges separate clipping algorithms into one efficient pipeline.<n>Experiments show that U-CESE delivers robust, consistent, and efficient performance in large-scale multimodal event retrieval.
• Score: 0.34040662197740373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieving events from large-scale video datasets is challenging due to complex temporal, spatial, and multimodal information. This paper presents U-CESE, our solution for the AI Challenge HCMC 2025, a Unified Clip-based Event Search Engine for multimodal event retrieval across diverse video sources. Building on CESE, U-CESE integrates its three modules into a single cohesive framework, ensuring consistent processing and retrieval across query types. A core component is the Unified Clipping Algorithm, which merges separate clipping algorithms into one efficient pipeline. To handle large-scale data, we propose DAKE, a lightweight, training-free keyframe extraction method using JPEG file size variations to identify significant scene changes. Finally, we introduce ReCap, a temporally consistent captioning framework inspired by Recurrent Neural Network, generating detailed and context-aware textual descriptions. Experiments show that U-CESE delivers robust, consistent, and efficient performance in large-scale multimodal event retrieval.

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