Efficient and High-Fidelity Omni Modality Retrieval
Multimodal retrieval is the task of aggregating information from queries across heterogeneous modalities to retrieve desired targets. State-of-the-art multimodal retrieval models can understand complex queries, yet they are typically limited to two modalities: text and vision. This limitation impedes the development of universal retrieval systems capable of comprehending queries that combine more than two modalities. To advance toward this goal, we present OmniRet, the first retrieval model capable of handling complex, composed queries spanning three key modalities: text, vision, and audio. Our OmniRet model addresses two critical challenges for universal retrieval: computational efficien...
| Authors | Chuong Huynh, Manh Luong, Abhinav Shrivastava |
|---|---|
| Primary category | cs.IR |
| Published | 2 March 2026 |
| Last updated at source | 24 March 2026 |
Models and datasets referencing this paper
- chuonghm/OmniRet-trainDataset
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