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UniIR: Training and Benchmarking Universal Multimodal Information Retrievers

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arxiv 2311.17136 v1 pith:TPAMRTPA submitted 2023-11-28 cs.CV cs.AIcs.CLcs.IR

Cong Wei , Yang Chen , Haonan Chen , Hexiang Hu , Ge Zhang , Jie Fu , Alan Ritter , Wenhu Chen This is my paper · ORCID
classification cs.CVcs.AIcs.CLcs.IR
keywords retrievalmultimodaluniirinformationtasksacrossdatasetsdiverse
verification ladder T0 review T1 audit T2 compute T3 formal
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Existing information retrieval (IR) models often assume a homogeneous format, limiting their applicability to diverse user needs, such as searching for images with text descriptions, searching for a news article with a headline image, or finding a similar photo with a query image. To approach such different information-seeking demands, we introduce UniIR, a unified instruction-guided multimodal retriever capable of handling eight distinct retrieval tasks across modalities. UniIR, a single retrieval system jointly trained on ten diverse multimodal-IR datasets, interprets user instructions to execute various retrieval tasks, demonstrating robust performance across existing datasets and zero-shot generalization to new tasks. Our experiments highlight that multi-task training and instruction tuning are keys to UniIR's generalization ability. Additionally, we construct the M-BEIR, a multimodal retrieval benchmark with comprehensive results, to standardize the evaluation of universal multimodal information retrieval.

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Cited by 5 Pith papers

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    A frozen multimodal LLM is extended with a retrieval adapter and an entity adapter to retrieve a relevant image and generate a supportive textual comment.

  5. Reconstructing Content with Collaborative Attention for Universal Multimodal Representation Learning

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