most citedConvolutional Bypasses Are Better Vision Transformer Adapters

62 citations · 76 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CL20231 cited

Prefix-Tuning Based Unsupervised Text Style Transfer

Huiyu Mai, Wenhao Jiang, Zhihong Deng

Unsupervised text style transfer aims at training a generative model that can alter the style of the input sentence while preserving its content without using any parallel data. In…

cs.CV20231 cited

Revisiting the Parameter Efficiency of Adapters from the Perspective of Precision Redundancy

Shibo Jie, Haoqing Wang, Zhi-Hong Deng

Current state-of-the-art results in computer vision depend in part on fine-tuning large pre-trained vision models. However, with the exponential growth of model sizes, the conventi…

cs.CL20231 cited

Dual-Alignment Pre-training for Cross-lingual Sentence Embedding

Ziheng Li, Shaohan Huang, Zihan Zhang +7

Recent studies have shown that dual encoder models trained with the sentence-level translation ranking task are effective methods for cross-lingual sentence embedding. However, our…

cs.CV20234 cited

Masked Image Modeling with Local Multi-Scale Reconstruction

Haoqing Wang, Yehui Tang, Yunhe Wang +3

Masked Image Modeling (MIM) achieves outstanding success in self-supervised representation learning. Unfortunately, MIM models typically have huge computational burden and slow lea…

cs.LG20235 cited

Are More Layers Beneficial to Graph Transformers?

Haiteng Zhao, Shuming Ma, Dongdong Zhang +2

Despite that going deep has proven successful in many neural architectures, the existing graph transformers are relatively shallow. In this work, we explore whether more layers are…

q-bio.BM20231 cited

Retrieved Sequence Augmentation for Protein Representation Learning

Chang Ma, Haiteng Zhao, Lin Zheng +7

Protein language models have excelled in a variety of tasks, ranging from structure prediction to protein engineering. However, proteins are highly diverse in functions and structu…