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U-CESE engine enhances multimodal event retrieval for AI Challenge

By PulseAugur Editorial · [2 sources] ·

Researchers have developed U-CESE, a Unified Clip-based Event Search Engine designed for the AI Challenge HCMC 2025. This system aims to improve the retrieval of events from large video datasets by integrating multiple modules into a cohesive framework. Key innovations include a Unified Clipping Algorithm for efficient processing, a DAKE method for lightweight keyframe extraction using JPEG file size variations, and ReCap, a captioning framework that generates temporally consistent descriptions. AI

IMPACT Introduces novel methods for efficient video event retrieval and keyframe extraction, potentially improving AI systems that process large video datasets.

RANK_REASON The cluster contains an academic paper detailing a new system and methods for multimodal event retrieval.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Duc-Nhuan Le, Hoang-Phuc Nguyen, Thanh-Duy Lam, Minh-Nhut Dang, Minh-Hoang Le ·

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

    arXiv:2605.23274v1 Announce Type: new 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 Searc…

  2. arXiv cs.CV TIER_1 English(EN) · Minh-Hoang Le ·

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

    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 d…

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