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VEDJE system enables efficient video-text retrieval with compact caches

By PulseAugur Editorial · [1 sources] ·

Researchers have developed VEDJE, a novel system for scalable video-text retrieval that significantly reduces computational costs. VEDJE achieves this by compressing video features into a compact, reusable cache, allowing for efficient matching against new queries. This approach improves retrieval accuracy on benchmarks like MSR-VTT, MSVD, DiDeMo, and ActivityNet, while drastically shrinking the data footprint per video. AI

IMPACT Enables more efficient and scalable multimodal search systems by reducing computational load and data storage requirements.

RANK_REASON The item describes a new research paper detailing a novel method for video-text retrieval. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

VEDJE system enables efficient video-text retrieval with compact caches

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The item describes a new research paper detailing a novel method for video-text retrieval. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    VEDJE: Video-Efficient Discriminative Joint Encoder for Scalable Video-Text Retrieval

    Finding the right video often requires distinguishing similar scenes in which different events occur. Joint matching improves retrieval, but processing rich video representations for each query is costly. VEDJE compresses features within sampled frames while keeping their represe…

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