Papers by Xujie Zhang
MiMIC: Mitigating Visual Modality Collapse in Universal Multimodal Retrieval While Avoiding Semantic Misalignment (2026.findings-acl)
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| Challenge: | Existing UMR methods can be broadly divided into two categories: early-fusion approaches, such as Marvel, which projects visual features into the language model space for integrating with text modality, and late-fusion methods, such UniVL-DR, which encode visual and textual inputs using separate encoders and obtain fused embeddings through addition. |
| Approach: | They propose to map different modalities into a shared embedding space for multi-modal retrieval. |
| Outcome: | Experiments on the WebQA+ and EVQA+ datasets show that MiMIC outperforms both early- and late-fusion approaches. |