Papers by Tingting Bi
Compressing then Matching: An Efficient Pre-training Paradigm for Multimodal Embedding (2026.acl-long)
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Da Li, Yuxiao Luo, Keping Bi, Jiafeng Guo, Wei Yuan, Biao Yang, Yan Wang, Fan Yang, Tingting Gao, Guorui Zhou
| Challenge: | Recent approaches demonstrate that MLLMs can be adapted into competitive embedding models via large-scale contrastive learning. |
| Approach: | They propose a compressed pre-training phase which serves as a warm-up stage for contrastive learning. |
| Outcome: | The proposed model achieves state-of-the-art among MLLMs of comparable size on the MMEB, realizing optimization in both efficiency and effectiveness. |
DocSpiral: A Platform for Integrated Assistive Document Annotation through Human-in-the-Spiral (2025.acl-demo)
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| Challenge: | Documents that are image-based are difficult to extract because of document variability. |
| Approach: | They propose a human-in-the-spiral assistive document annotation platform to extract structured data from document collections. |
| Outcome: | The proposed framework reduces annotation time by at least 41% while showing consistent performance gains over three iterations. |