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VEDJE encoder boosts video-text retrieval with compressed features

By PulseAugur Editorial · [1 sources] ·

Researchers have developed VEDJE, a novel Video-Efficient Discriminative Joint Encoder designed to enhance the scalability and efficiency of video-text retrieval. This method compresses video features into a reusable cache, significantly reducing computational costs during retrieval. By incorporating a feature-change prediction signal during training, VEDJE improves retrieval accuracy without increasing query-time workload. The system demonstrates strong performance across multiple benchmarks, including MSR-VTT, MSVD, DiDeMo, and ActivityNet, achieving competitive recall rates while drastically shrinking the per-video cache size. AI

IMPACT Enhances efficiency and scalability for video search applications by enabling retrieval on compressed video data.

RANK_REASON This is a research paper detailing a new method for video-text retrieval. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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

VEDJE encoder boosts video-text retrieval with compressed features

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This is a research paper detailing a new method for video-text retrieval. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Shahaf Wagner, Gabriele Serussi, Dan Ben Ami, Tomer Galanti, Chaim Baskin ·

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

    arXiv:2610.11850v1 Announce Type: new Abstract: 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 w…

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