34 citations · 37 across the 12 of their papers we have counts for
12 papers
FSE: Continual Learning for Named Entity Recognition by Fast-Slow Experts
Yunan Zhang, Yang Fan, Heng Li +2
Continual Learning for Named Entity Recognition (CLNER) enable models to incrementally learn new entity types without forgetting previously acquired ones. However, existing methods…
WaterSearch: Exploring Seed Pooling for Improving the Quality-Detectability Trade-off in LLM Watermarking
Yukang Lin, Jiahao Shao, Shuoran Jiang +5
Watermarking acts as a critical safeguard in text generated by Large Language Models (LLMs). By embedding identifiable signals into model outputs, watermarking enables reliable att…
GeRe: Towards Efficient Anti-Forgetting in Continual Learning of LLM via General Samples Replay
Yunan Zhang, Shuoran Jiang, Mengchen Zhao +4
The continual learning capability of large language models (LLMs) is crucial for advancing artificial general intelligence. However, continual fine-tuning LLMs across various domai…
D2Dewarp: Dual Dimensions Geometric Representation Learning Based Document Image Dewarping
Heng Li, Xiangping Wu, Qingcai Chen
Document image dewarping remains a challenging task in the deep learning era. While existing methods have improved by leveraging text line awareness, they typically focus only on a…
Document Image Rectification Bases on Self-Adaptive Multitask Fusion
Heng Li, Xiangping Wu, Qingcai Chen
Deformed document image rectification is essential for real-world document understanding tasks, such as layout analysis and text recognition. However, current multi-task methods --…
CrossFormer: Cross-Segment Semantic Fusion for Document Segmentation
Tongke Ni, Yang Fan, Junru Zhou +2
Text semantic segmentation involves partitioning a document into multiple paragraphs with continuous semantics based on the subject matter, contextual information, and document str…
ZO-AdaMU Optimizer: Adapting Perturbation by the Momentum and Uncertainty in Zeroth-order Optimization
Shuoran Jiang, Qingcai Chen, Youchen Pan +5
Lowering the memory requirement in full-parameter training on large models has become a hot research area. MeZO fine-tunes the large language models (LLMs) by just forward passes i…
TDeLTA: A Light-weight and Robust Table Detection Method based on Learning Text Arrangement
Yang Fan, Xiangping Wu, Qingcai Chen +4
The diversity of tables makes table detection a great challenge, leading to existing models becoming more tedious and complex. Despite achieving high performance, they often overfi…
Confounder Balancing in Adversarial Domain Adaptation for Pre-Trained Large Models Fine-Tuning
Shuoran Jiang, Qingcai Chen, Yang Xiang +2
The excellent generalization, contextual learning, and emergence abilities in the pre-trained large models (PLMs) handle specific tasks without direct training data, making them th…
Decomposing Word Embedding with the Capsule Network
Xin Liu, Qingcai Chen, Yan Liu +4
Word sense disambiguation tries to learn the appropriate sense of an ambiguous word in a given context. The existing pre-trained language methods and the methods based on multi-emb…
CNN based music emotion classification
Xin Liu, Qingcai Chen, Xiangping Wu +2
Music emotion recognition (MER) is usually regarded as a multi-label tagging task, and each segment of music can inspire specific emotion tags. Most researchers extract acoustic fe…
Stroke Sequence-Dependent Deep Convolutional Neural Network for Online Handwritten Chinese Character Recognition
Baotian Hu, Xin Liu, Xiangping Wu +1
In this paper, we propose a novel model, named Stroke Sequence-dependent Deep Convolutional Neural Network (SSDCNN), using the stroke sequence information and eight-directional fea…