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Text-to-video retrieval models struggle with complex queries

By PulseAugur Editorial · [1 sources] ·

A new paper analyzes the performance plateau in text-to-video retrieval systems, evaluating 14 state-of-the-art methods across three datasets. The research found that simpler, clearer captions describing single actions or attributes yield higher retrieval recall. Complex events and multi-step activities remain challenging for current models, with attention-driven architectures showing an advantage for temporally dependent queries. AI

IMPACT Identifies key dataset factors and query complexities that hinder text-to-video retrieval, guiding future model development.

RANK_REASON This is a research paper published on arXiv analyzing existing text-to-video retrieval methods. [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 →

Text-to-video retrieval models struggle with complex queries

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This is a research paper published on arXiv analyzing existing text-to-video retrieval methods. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Maria-Eirini Pegia, Dimitrios Stefanopoulos, Bj\"orn {\TH}\'or J\'onsson, Anastasia Moumtzidou, Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis Kompatsiaris ·

    Understanding the Performance Plateau in Text-to-Video Retrieval: A Comprehensive Empirical and Linguistic Analysis

    arXiv:2605.00826v1 Announce Type: cross Abstract: Text-to-video retrieval enables users to find relevant video content using natural language queries, a task that has grown increasingly important with the rapid expansion of online video. Over the past six years, research has prod…

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