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New datasets and models advance text-to-long-video retrieval capabilities

By PulseAugur Editorial · [3 sources] ·

Researchers have introduced MELON, a new large-scale dataset designed for multi-event text-to-long-video retrieval, addressing the limitations of existing datasets that primarily focus on short clips with single events. MELON annotates multiple event intervals within long videos, enabling models to better understand complex, multi-event structures. Concurrently, a framework named TAME has been proposed, which enhances text-video retrieval by integrating temporal modeling into CLIP-based architectures. TAME utilizes Mixture-of-Experts layers and Frame-Temporal tokens to capture both local frame details and long-range temporal dependencies, showing improved performance on standard retrieval benchmarks. AI

IMPACT Advances in text-video retrieval could lead to more sophisticated content search and recommendation systems.

RANK_REASON Two research papers introducing new datasets and frameworks for text-video retrieval.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New datasets and models advance text-to-long-video retrieval capabilities

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COVERAGE [3]

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

    TAME: Temporal-Aware Mixture-of-Experts for Text-Video Retrieval

    Text-Video Retrieval (TVR) retrieves videos that match a natural-language query, but extending image-text models such as CLIP to videos is fundamentally limited by the lack of temporal modeling. Videos exhibit frame-wise heterogeneity in appearance and motion, and compressing all…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · KyungTae Lim ·

    MELON: A Large-Scale Dataset for Multi-Event Text-to-Long-Video Retrieval

    Existing text-video retrieval datasets primarily consist of short-form clips containing a single dominant event. While suitable for measuring basic vision-language alignment, they are limited in capturing real-world retrieval scenarios, where long-form videos naturally contain mu…

  3. arXiv cs.CV TIER_1 English(EN) · Uicheol Jung, Juyoung Hong, Hojung Kwon, Yukyung Choi ·

    TAME: Temporal-Aware Mixture-of-Experts for Text-Video Retrieval

    arXiv:2609.02204v1 Announce Type: new Abstract: Text-Video Retrieval (TVR) retrieves videos that match a natural-language query, but extending image-text models such as CLIP to videos is fundamentally limited by the lack of temporal modeling. Videos exhibit frame-wise heterogenei…

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