Search Results for author: Fan Bai

Found 16 papers, 6 papers with code

C3-STISR: Scene Text Image Super-resolution with Triple Clues

1 code implementation29 Apr 2022 Minyi Zhao, Miao Wang, Fan Bai, Bingjia Li, Jie Wang, Shuigeng Zhou

In this paper, we present a novel method C3-STISR that jointly exploits the recognizer's feedback, visual and linguistical information as clues to guide super-resolution.

Image Super-Resolution Language Modelling

An age-of-infection model with both symptomatic and asymptomatic infections

no code implementations3 Nov 2021 Fan Bai

We formulate a general age-of-infection epidemic model with two pathways: the symptomatic infections and the asymptomatic infections.

Hierarchical Policy for Non-prehensile Multi-object Rearrangement with Deep Reinforcement Learning and Monte Carlo Tree Search

1 code implementation18 Sep 2021 Fan Bai, Fei Meng, Jianbang Liu, Jiankun Wang, Max Q. -H. Meng

Non-prehensile multi-object rearrangement is a robotic task of planning feasible paths and transferring multiple objects to their predefined target poses without grasping.

Pre-train or Annotate? Domain Adaptation with a Constrained Budget

1 code implementation EMNLP 2021 Fan Bai, Alan Ritter, Wei Xu

Our experiments suggest task-specific data annotation should be part of an economical strategy when adapting an NLP model to a new domain.

Domain Adaptation

Federated Face Recognition

no code implementations6 May 2021 Fan Bai, Jiaxiang Wu, Pengcheng Shen, Shaoxin Li, Shuigeng Zhou

Face recognition has been extensively studied in computer vision and artificial intelligence communities in recent years.

Face Recognition Federated Learning

Process-Level Representation of Scientific Protocols with Interactive Annotation

2 code implementations EACL 2021 Ronen Tamari, Fan Bai, Alan Ritter, Gabriel Stanovsky

We develop Process Execution Graphs (PEG), a document-level representation of real-world wet lab biochemistry protocols, addressing challenges such as cross-sentence relations, long-range coreference, grounding, and implicit arguments.

Relation Extraction


no code implementations CUHK Course IERG5350 2020 Fan Bai, Fei Meng

The literature about scene rearrangement focus on developing a move planner totransform a pair of layouts on a limited plane.


Text Recognition in Real Scenarios with a Few Labeled Samples

no code implementations22 Jun 2020 Jinghuang Lin, Zhanzhan Cheng, Fan Bai, Yi Niu, ShiLiang Pu, Shuigeng Zhou

Scene text recognition (STR) is still a hot research topic in computer vision field due to its various applications.

Domain Adaptation Scene Text Recognition

Structured Minimally Supervised Learning for Neural Relation Extraction

1 code implementation NAACL 2019 Fan Bai, Alan Ritter

Our approach achieves state-of-the-art results on minimally supervised sentential relation extraction, outperforming a number of baselines, including a competitive approach that uses the attention layer of a purely neural model.

Relation Extraction

Spatial clustering and common regulatory elements correlate with coordinated gene expression

no code implementations18 Jan 2019 Jingyu Zhang, HengYu Chen, Ruoyan Li, David A. Taft, Guang Yao, Fan Bai, Jianhua Xing

Many cellular responses to surrounding cues require temporally concerted transcriptional regulation of multiple genes.

Edit Probability for Scene Text Recognition

no code implementations CVPR 2018 Fan Bai, Zhanzhan Cheng, Yi Niu, ShiLiang Pu, Shuigeng Zhou

The advantage lies in that the training process can focus on the missing, superfluous and unrecognized characters, and thus the impact of the misalignment problem can be alleviated or even overcome.

Frame Scene Text Recognition

AON: Towards Arbitrarily-Oriented Text Recognition

1 code implementation CVPR 2018 Zhanzhan Cheng, Yangliu Xu, Fan Bai, Yi Niu, ShiLiang Pu, Shuigeng Zhou

Existing methods on text recognition mainly work with regular (horizontal and frontal) texts and cannot be trivially generalized to handle irregular texts.

Optical Character Recognition Scene Text Recognition

Focusing Attention: Towards Accurate Text Recognition in Natural Images

no code implementations ICCV 2017 Zhanzhan Cheng, Fan Bai, Yunlu Xu, Gang Zheng, ShiLiang Pu, Shuigeng Zhou

FAN consists of two major components: an attention network (AN) that is responsible for recognizing character targets as in the existing methods, and a focusing network (FN) that is responsible for adjusting attention by evaluating whether AN pays attention properly on the target areas in the images.

Scene Text Recognition

Label Propagation on K-partite Graphs with Heterophily

no code implementations21 Jan 2017 Dingxiong Deng, Fan Bai, Yiqi Tang, Shuigeng Zhou, Cyrus Shahabi, Linhong Zhu

In this paper, for the first time, we study label propagation in heterogeneous graphs under heterophily assumption.

Online Learning for Wireless Distributed Computing

no code implementations9 Nov 2016 Yi-Hsuan Kao, Kwame Wright, Bhaskar Krishnamachari, Fan Bai

To the best of our knowledge, MABSTA is the first online algorithm in this domain of task assignment problems and provides provable performance guarantee.

Distributed Computing online learning

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