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Zhonghua Li

6 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author6

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.CV2
ORCID 0009-0008-0395-2820
same name
  • Zhonghua Li — 15 papers, h 8
  • Zhonghua Li — 5 papers, h 4
  • Zhonghua Li — 4 papers, h 1
  • Zhonghua Li — 3 papers, h 4
  • Zhonghua Li — 3 papers
  • Zhonghua Li — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedFashionSAP: Symbols and Attributes Prompt for Fine-grained Fashion Vision-Language Pre-training

2 citations · 3 across the 6 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2024

HiLight: A Hierarchy-aware Light Global Model with Hierarchical Local ConTrastive Learning

Zhijian Chen, Zhonghua Li, Jianxin Yang +1

Hierarchical text classification (HTC) is a special sub-task of multi-label classification (MLC) whose taxonomy is constructed as a tree and each sample is assigned with at least o…

cs.CL2024

FecTek: Enhancing Term Weight in Lexicon-Based Retrieval with Feature Context and Term-level Knowledge

Zunran Wang, Zhonghua Li, Wei Shen +2

Lexicon-based retrieval has gained siginificant popularity in text retrieval due to its efficient and robust performance. To further enhance performance of lexicon-based retrieval,…

cs.CL2023

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…

cs.CL2023

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.