49 citations · 111 across the 31 of their papers we have counts for
9 papers · 1 filter
Black-Box Tuning of Vision-Language Models with Effective Gradient Approximation
Zixian Guo, Yuxiang Wei, Ming Liu +4
Parameter-efficient fine-tuning (PEFT) methods have provided an effective way for adapting large vision-language models to specific tasks or scenarios. Typically, they learn a very…
Decoupled Textual Embeddings for Customized Image Generation
Yufei Cai, Yuxiang Wei, Zhilong Ji +3
Customized text-to-image generation, which aims to learn user-specified concepts with a few images, has drawn significant attention recently. However, existing methods usually suff…
Patch Is Not All You Need
Changzhen Li, Jie Zhang, Yang Wei +3
Vision Transformers have achieved great success in computer visions, delivering exceptional performance across various tasks. However, their inherent reliance on sequential input e…
Semantic Graph Representation Learning for Handwritten Mathematical Expression Recognition
Zhuang Liu, Ye Yuan, Zhilong Ji +2
Handwritten mathematical expression recognition (HMER) has attracted extensive attention recently. However, current methods cannot explicitly study the interactions between differe…
LRANet: Towards Accurate and Efficient Scene Text Detection with Low-Rank Approximation Network
Yuchen Su, Zhineng Chen, Zhiwen Shao +5
Recently, regression-based methods, which predict parameterized text shapes for text localization, have gained popularity in scene text detection. However, the existing parameteriz…
Inferring and Leveraging Parts from Object Shape for Improving Semantic Image Synthesis
Yuxiang Wei, Zhilong Ji, Xiaohe Wu +3
Despite the progress in semantic image synthesis, it remains a challenging problem to generate photo-realistic parts from input semantic map. Integrating part segmentation map can…
Masked and Permuted Implicit Context Learning for Scene Text Recognition
Xiaomeng Yang, Zhi Qiao, Jin Wei +2
Scene Text Recognition (STR) is difficult because of the variations in text styles, shapes, and backgrounds. Though the integration of linguistic information enhances models' perfo…
CCLAP: Controllable Chinese Landscape Painting Generation via Latent Diffusion Model
Zhongqi Wang, Jie Zhang, Zhilong Ji +2
With the development of deep generative models, recent years have seen great success of Chinese landscape painting generation. However, few works focus on controllable Chinese land…
ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation
Yuxiang Wei, Yabo Zhang, Zhilong Ji +3
In addition to the unprecedented ability in imaginary creation, large text-to-image models are expected to take customized concepts in image generation. Existing works generally le…