Search Results for author: Zhonghua Li

Found 8 papers, 4 papers with code

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

no code implementations18 Apr 2024 Zunran Wang, Zhonghua Li, Wei Shen, Qi Ye, Liqiang Nie

To effectively enrich the feature context representations of term weight, the Feature Context Module (FCM) is introduced, which leverages the power of BERT's representation to determine dynamic weights for each element in the embedding.

Contrastive Learning Retrieval +1

Plug-and-Play Document Modules for Pre-trained Models

1 code implementation28 May 2023 Chaojun Xiao, Zhengyan Zhang, Xu Han, Chi-Min Chan, Yankai Lin, Zhiyuan Liu, Xiangyang Li, Zhonghua Li, Zhao Cao, Maosong Sun

By inserting document plugins into the backbone PTM for downstream tasks, we can encode a document one time to handle multiple tasks, which is more efficient than conventional encoding-task coupling methods that simultaneously encode documents and input queries using task-specific encoders.

Question Answering

Rethinking Dense Retrieval's Few-Shot Ability

1 code implementation12 Apr 2023 Si Sun, Yida Lu, Shi Yu, Xiangyang Li, Zhonghua Li, Zhao Cao, Zhiyuan Liu, Deiming Ye, Jie Bao

Moreover, the dataset is disjointed into base and novel classes, allowing DR models to be continuously trained on ample data from base classes and a few samples in novel classes.

Retrieval

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

1 code implementation CVPR 2023 Yunpeng Han, Lisai Zhang, Qingcai Chen, Zhijian Chen, Zhonghua Li, Jianxin Yang, Zhao Cao

We propose a method for fine-grained fashion vision-language pre-training based on fashion Symbols and Attributes Prompt (FashionSAP) to model fine-grained multi-modalities fashion attributes and characteristics.

Attribute

FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection

2 code implementations21 Nov 2021 Zhonghua Li, Biao Hou, Zitong Wu, Licheng Jiao, Bo Ren, Chen Yang

We convert a lightweight FCOSR model to TensorRT format, which achieves 73. 93 mAP on DOTA1. 0 at a speed of 10. 68 FPS on Jetson Xavier NX with single scale.

object-detection Object Detection +1

MarlRank: Multi-agent Reinforced Learning to Rank

no code implementations15 Sep 2019 Shihao Zou, Zhonghua Li, Mohammad Akbari, Jun Wang, Peng Zhang

By defining reward as a function of NDCG, we can optimize our model directly on the ranking performance measure.

Document Ranking Learning-To-Rank

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