most citedType-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing

2 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CY20232 cited

Blockchain-based Decentralized Co-governance: Innovations and Solutions for Sustainable Crowdfunding

Bingyou Chen, Yu Luo, Jieni Li +5

This thesis provides an in-depth exploration of the Decentralized Co-governance Crowdfunding (DCC) Ecosystem, a novel solution addressing prevailing challenges in conventional crow…

cs.CV2023

Perception and Semantic Aware Regularization for Sequential Confidence Calibration

Zhenghua Peng, Yu Luo, Tianshui Chen +2

Deep sequence recognition (DSR) models receive increasing attention due to their superior application to various applications. Most DSR models use merely the target sequences as su…

cs.AI20232 cited

Context-Aware Selective Label Smoothing for Calibrating Sequence Recognition Model

Shuangping Huang, Yu Luo, Zhenzhou Zhuang +3

Despite the success of deep neural network (DNN) on sequential data (i.e., scene text and speech) recognition, it suffers from the over-confidence problem mainly due to overfitting…

cs.CL20222 cited

Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing

Xinyu Zuo, Haijin Liang, Ning Jing +3

Fine-grained entity typing (FET) aims to deduce specific semantic types of the entity mentions in text. Modern methods for FET mainly focus on learning what a certain type looks li…

cs.CL2022

ChiQA: A Large Scale Image-based Real-World Question Answering Dataset for Multi-Modal Understanding

Bingning Wang, Feiyang Lv, Ting Yao +4

Visual question answering is an important task in both natural language and vision understanding. However, in most of the public visual question answering datasets such as VQA, CLE…