3 citations · 4 across the 6 of their papers we have counts for
6 papers
DualToken-ViT: Position-aware Efficient Vision Transformer with Dual Token Fusion
Zhenzhen Chu, Jiayu Chen, Cen Chen +4
Self-attention-based vision transformers (ViTs) have emerged as a highly competitive architecture in computer vision. Unlike convolutional neural networks (CNNs), ViTs are capable…
Making Small Language Models Better Multi-task Learners with Mixture-of-Task-Adapters
Yukang Xie, Chengyu Wang, Junbing Yan +3
Recently, Large Language Models (LLMs) have achieved amazing zero-shot learning performance over a variety of Natural Language Processing (NLP) tasks, especially for text generativ…
PAI-Diffusion: Constructing and Serving a Family of Open Chinese Diffusion Models for Text-to-image Synthesis on the Cloud
Chengyu Wang, Zhongjie Duan, Bingyan Liu +4
Text-to-image synthesis for the Chinese language poses unique challenges due to its large vocabulary size, and intricate character relationships. While existing diffusion models ha…
Knowledgeable In-Context Tuning: Exploring and Exploiting Factual Knowledge for In-Context Learning
Jianing Wang, Chengyu Wang, Chuanqi Tan +2
Large language models (LLMs) enable in-context learning (ICL) by conditioning on a few labeled training examples as a text-based prompt, eliminating the need for parameter updates…
Uncertainty-aware Self-training for Low-resource Neural Sequence Labeling
Jianing Wang, Chengyu Wang, Jun Huang +2
Neural sequence labeling (NSL) aims at assigning labels for input language tokens, which covers a broad range of applications, such as named entity recognition (NER) and slot filli…
Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning
Shuzhen Rao, Jun Huang, Zengming Tang
In cross-domain few-shot learning, the core issue is that the model trained on source domains struggles to generalize to the target domain, especially when the domain shift is larg…