3 citations · 7 across the 5 of their papers we have counts for
5 papers
Exploring Similarity between Neural and LLM Trajectories in Language Processing
Xin Xiao, Kaiwen Wei, Jiang Zhong +2
Understanding the similarity between large language models (LLMs) and human brain activity is crucial for advancing both AI and cognitive neuroscience. In this study, we provide a…
DAPE: Dual-Stage Parameter-Efficient Fine-Tuning for Consistent Video Editing with Diffusion Models
Junhao Xia, Chaoyang Zhang, Yecheng Zhang +4
Video generation based on diffusion models presents a challenging multimodal task, with video editing emerging as a pivotal direction in this field. Recent video editing approaches…
5%>100%: Breaking Performance Shackles of Full Fine-Tuning on Visual Recognition Tasks
Dongshuo Yin, Leiyi Hu, Bin Li +2
Pre-training & fine-tuning can enhance the transferring efficiency and performance in visual tasks. Recent delta-tuning methods provide more options for visual classification tasks…
Adapter is All You Need for Tuning Visual Tasks
Dongshuo Yin, Leiyi Hu, Bin Li +1
Pre-training & fine-tuning can enhance the transferring efficiency and performance in visual tasks. Recent delta-tuning methods provide more options for visual classification tasks…
Parameter-efficient is not sufficient: Exploring Parameter, Memory, and Time Efficient Adapter Tuning for Dense Predictions
Dongshuo Yin, Xueting Han, Bin Li +2
Pre-training & fine-tuning is a prevalent paradigm in computer vision (CV). Recently, parameter-efficient transfer learning (PETL) methods have shown promising performance in adapt…