1.1k citations · 1.1k across the 38 of their papers we have counts for
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One-Dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications
Mengyao Lyu, Yuhong Yang, Haiwen Hong +6
The prevalent use of commercial and open-source diffusion models (DMs) for text-to-image generation prompts risk mitigation to prevent undesired behaviors. Existing concept erasing…
Towards Efficient Vision-Language Tuning: More Information Density, More Generalizability
Tianxiang Hao, Mengyao Lyu, Hui Chen +4
With the advancement of large pre-trained vision-language models, effectively transferring the knowledge embedded within these foundational models to downstream tasks has become a…
RepViT-SAM: Towards Real-Time Segmenting Anything
Ao Wang, Hui Chen, Zijia Lin +2
Segment Anything Model (SAM) has shown impressive zero-shot transfer performance for various computer vision tasks recently. However, its heavy computation costs remain daunting fo…
MiLe Loss: a New Entropy-Weighed Loss for Mitigating the Bias of Learning Difficulties in Large Language Models
Zhenpeng Su, Xing Wu, Xue Bai +5
Generative language models are usually pretrained on large text corpus via predicting the next token (i.e., sub-word/word/phrase) given the previous ones. Recent works have demonst…