Any Information Is Just Worth One Single Screenshot: Unifying Search With Visualized Information Retrieval

• URL: http://arxiv.org/abs/2502.11431v1
• Date: Mon, 17 Feb 2025 04:40:15 GMT
• Title: Any Information Is Just Worth One Single Screenshot: Unifying Search With Visualized Information Retrieval
• Authors: Ze Liu, Zhengyang Liang, Junjie Zhou, Zheng Liu, Defu Lian,
• Abstract summary: We define an emerging IR paradigm called textitVisualized Information Retrieval, or textbfVis-IR, where multimodal information is jointly represented by a unified visual format.<n>We make three key contributions for Vis-IR. First, we create textbfVIRA (Vis-IR Aggregation), a large-scale dataset comprising a vast collection of screenshots from diverse sources.<n>Second, we develop textbfUniSE (Universal Screenshot Embeddings), a family of retrieval models that enable screenshots to query or be queried across
• Score: 33.6584861832657
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the popularity of multimodal techniques, it receives growing interests to acquire useful information in visual forms. In this work, we formally define an emerging IR paradigm called \textit{Visualized Information Retrieval}, or \textbf{Vis-IR}, where multimodal information, such as texts, images, tables and charts, is jointly represented by a unified visual format called \textbf{Screenshots}, for various retrieval applications. We further make three key contributions for Vis-IR. First, we create \textbf{VIRA} (Vis-IR Aggregation), a large-scale dataset comprising a vast collection of screenshots from diverse sources, carefully curated into captioned and question-answer formats. Second, we develop \textbf{UniSE} (Universal Screenshot Embeddings), a family of retrieval models that enable screenshots to query or be queried across arbitrary data modalities. Finally, we construct \textbf{MVRB} (Massive Visualized IR Benchmark), a comprehensive benchmark covering a variety of task forms and application scenarios. Through extensive evaluations on MVRB, we highlight the deficiency from existing multimodal retrievers and the substantial improvements made by UniSE. Our work will be shared with the community, laying a solid foundation for this emerging field.

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