Beyond Coarse-Grained Matching in Video-Text Retrieval

Aozhu Chen; Hazel Doughty; Xirong Li; Cees G. M. Snoek

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arXiv · 2410.12407

Beyond Coarse-Grained Matching in Video-Text Retrieval

Aozhu Chen·Hazel Doughty·Xirong Li·Cees G. M. Snoek

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Abstract

Video-text retrieval has seen significant advancements, yet the ability of models to discern subtle differences in captions still requires verification. In this paper, we introduce a new approach for fine-grained evaluation. Our approach can be applied to existing datasets by automatically generating hard negative test captions with subtle single-word variations across nouns, verbs, adjectives, adverbs, and prepositions. We perform comprehensive experiments using four state-of-the-art models across two standard benchmarks (MSR-VTT and VATEX) and two specially curated datasets enriched with detailed descriptions (VLN-UVO and VLN-OOPS), resulting in a number of novel insights: 1) our analyses show that the current evaluation benchmarks fall short in detecting a model's ability to perceive subtle single-word differences, 2) our fine-grained evaluation highlights the difficulty models face in distinguishing such subtle variations. To enhance fine-grained understanding, we propose a new baseline that can be easily combined with current methods. Experiments on our fine-grained evaluations demonstrate that this approach enhances a model's ability to understand fine-grained differences.

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Aozhu Chen, Hazel Doughty, Xirong Li, Cees G. M. Snoek. 2024-10-17. Beyond Coarse-Grained Matching in Video-Text Retrieval. https://arxiv.org/abs/2410.12407

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