Fine-grained Video-Text Retrieval: A New Benchmark and Method

• URL: http://arxiv.org/abs/2501.00513v1
• Date: Tue, 31 Dec 2024 15:53:50 GMT
• Title: Fine-grained Video-Text Retrieval: A New Benchmark and Method
• Authors: Yifan Xu, Xinhao Li, Yichun Yang, Rui Huang, Limin Wang,
• Abstract summary: We present FIBER, a FIne-grained BEnchmark for text to video Retrieval, containing 1,000 videos sourced from FineAction dataset.<n>Uniquely, our FIBER benchmark provides detailed human-annotated spatial annotations and temporal annotations for each video.<n>Experiment results show that our Video Large Language (VLLE) performs comparably to CLIP-based models on traditional benchmarks.
• Score: 25.2967056489715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ability of perceiving fine-grained spatial and temporal information is crucial for video-language retrieval. However, the existing video retrieval benchmarks, such as MSRVTT and MSVD, fail to efficiently evaluate the fine-grained retrieval ability of video-language models (VLMs) due to a lack of detailed annotations. To address this problem, we present FIBER, a FIne-grained BEnchmark for text to video Retrieval, containing 1,000 videos sourced from the FineAction dataset. Uniquely, our FIBER benchmark provides detailed human-annotated spatial annotations and temporal annotations for each video, making it possible to independently evaluate the spatial and temporal bias of VLMs on video retrieval task. Besides, we employ a text embedding method to unlock the capability of fine-grained video-language understanding of Multimodal Large Language Models (MLLMs). Surprisingly, the experiment results show that our Video Large Language Encoder (VLLE) performs comparably to CLIP-based models on traditional benchmarks and has a stronger capability of fine-grained representation with lower spatial-temporal bias. Project page: https://fiber-bench.github.io.

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