20 citations · 37 across the 7 of their papers we have counts for
7 papers
GPTFace: Generative Pre-training of Facial-Linguistic Transformer by Span Masking and Weakly Correlated Text-image Data
Yudong Li, Hao Li, Xianxu Hou +1
Compared to the prosperity of pre-training models in natural image understanding, the research on large-scale pre-training models for facial knowledge learning is still limited. Cu…
LightQANet: Quantized and Adaptive Feature Learning for Low-Light Image Enhancement
Xu Wu, Zhihui Lai, Xianxu Hou +3
Low-light image enhancement (LLIE) aims to improve illumination while preserving high-quality color and texture. However, existing methods often fail to extract reliable feature re…
Low-Light Enhancement Effect on Classification and Detection: An Empirical Study
Xu Wu, Zhihui Lai, Zhou Jie +4
Low-light images are commonly encountered in real-world scenarios, and numerous low-light image enhancement (LLIE) methods have been proposed to improve the visibility of these ima…
Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning
Hongze Zhu, Guoyang Xie, Chengbin Hou +4
High-resolution point clouds~(HRPCD) anomaly detection~(AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods th…
Dynamic data sampler for cross-language transfer learning in large language models
Yudong Li, Yuhao Feng, Wen Zhou +4
Large Language Models (LLMs) have gained significant attention in the field of natural language processing (NLP) due to their wide range of applications. However, training LLMs for…
CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement
Xu Wu, XianXu Hou, Zhihui Lai +4
Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness de…
Deep Feature Consistent Variational Autoencoder
Xianxu Hou, Linlin Shen, Ke Sun +1
We present a novel method for constructing Variational Autoencoder (VAE). Instead of using pixel-by-pixel loss, we enforce deep feature consistency between the input and the output…