2 citations · 6 across the 6 of their papers we have counts for
6 papers
Plug-and-Play Document Modules for Pre-trained Models
Chaojun Xiao, Zhengyan Zhang, Xu Han +7
Large-scale pre-trained models (PTMs) have been widely used in document-oriented NLP tasks, such as question answering. However, the encoding-task coupling requirement results in t…
Rethinking Dense Retrieval's Few-Shot Ability
Si Sun, Yida Lu, Shi Yu +6
Few-shot dense retrieval (DR) aims to effectively generalize to novel search scenarios by learning a few samples. Despite its importance, there is little study on specialized datas…
FashionSAP: Symbols and Attributes Prompt for Fine-grained Fashion Vision-Language Pre-training
Yunpeng Han, Lisai Zhang, Qingcai Chen +4
Fashion vision-language pre-training models have shown efficacy for a wide range of downstream tasks. However, general vision-language pre-training models pay less attention to fin…
Refined Vision-Language Modeling for Fine-grained Multi-modal Pre-training
Lisai Zhang, Qingcai Chen, Zhijian Chen +3
Fine-grained supervision based on object annotations has been widely used for vision and language pre-training (VLP). However, in real-world application scenarios, aligned multi-mo…
VLDeformer: Vision-Language Decomposed Transformer for Fast Cross-Modal Retrieval
Lisai Zhang, Hongfa Wu, Qingcai Chen +6
Cross-model retrieval has emerged as one of the most important upgrades for text-only search engines (SE). Recently, with powerful representation for pairwise text-image inputs via…
MarlRank: Multi-agent Reinforced Learning to Rank
Shihao Zou, Zhonghua Li, Mohammad Akbari +2
When estimating the relevancy between a query and a document, ranking models largely neglect the mutual information among documents. A common wisdom is that if two documents are si…