1 code implementation • 21 May 2024 • Zhiyu Tan, Mengping Yang, Luozheng Qin, Hao Yang, Ye Qian, Qiang Zhou, Cheng Zhang, Hao Li
Moreover, the model capacity of the text encoder from CLIP is relatively limited compared to Large Language Models (LLMs), which offer multilingual input, accommodate longer context, and achieve superior text representation.
1 code implementation • 27 Mar 2024 • Yanbing Zhang, Mengping Yang, Qin Zhou, Zhe Wang
However, an intriguing problem persists: Is it possible to capture multiple, novel concepts from one single reference image?
1 code implementation • 30 Aug 2023 • Mengping Yang, Zhe Wang, Wenyi Feng, Qian Zhang, Ting Xiao
Furthermore, the frequency awareness of the model is reinforced by encouraging the model to distinguish frequency signals.
1 code implementation • 31 Jul 2023 • Mengping Yang, Zhe Wang
Despite numerous efforts to enhance training stability and synthesis quality in the limited data scenarios, there is a lack of a systematic survey that provides 1) a clear problem definition, critical challenges, and taxonomy of various tasks; 2) an in-depth analysis on the pros, cons, and remain limitations of existing literature; as well as 3) a thorough discussion on the potential applications and future directions in the field of image synthesis under limited data.
1 code implementation • 11 Oct 2022 • Mengping Yang, Zhe Wang, Ziqiu Chi, Yanbing Zhang
Training GANs under limited data often leads to discriminator overfitting and memorization issues, causing divergent training.
1 code implementation • 15 Jul 2022 • Mengping Yang, Zhe Wang, Ziqiu Chi, Wenyi Feng
Concretely, we disentangle encoded features into multiple frequency components and perform low-frequency skip connections to preserve outline and structural information.