78 citations · 102 across the 10 of their papers we have counts for
10 papers
A Comparative Study of Perceptual Quality Metrics for Audio-driven Talking Head Videos
Weixia Zhang, Chengguang Zhu, Jingnan Gao +3
The rapid advancement of Artificial Intelligence Generated Content (AIGC) technology has propelled audio-driven talking head generation, gaining considerable research attention for…
When No-Reference Image Quality Models Meet MAP Estimation in Diffusion Latents
Weixia Zhang, Dingquan Li, Guangtao Zhai +2
Contemporary no-reference image quality assessment (NR-IQA) models can effectively quantify perceived image quality, often achieving strong correlations with human perceptual score…
Few-Shot Class-Incremental Learning with Prior Knowledge
Wenhao Jiang, Duo Li, Menghan Hu +3
To tackle the issues of catastrophic forgetting and overfitting in few-shot class-incremental learning (FSCIL), previous work has primarily concentrated on preserving the memory of…
Uncertainty-aware Sampling for Long-tailed Semi-supervised Learning
Kuo Yang, Duo Li, Menghan Hu +3
For semi-supervised learning with imbalance classes, the long-tailed distribution of data will increase the model prediction bias toward dominant classes, undermining performance o…
NeRFVS: Neural Radiance Fields for Free View Synthesis via Geometry Scaffolds
Chen Yang, Peihao Li, Zanwei Zhou +5
We present NeRFVS, a novel neural radiance fields (NeRF) based method to enable free navigation in a room. NeRF achieves impressive performance in rendering images for novel views…
Head3D: Complete 3D Head Generation via Tri-plane Feature Distillation
Yuhao Cheng, Yichao Yan, Wenhan Zhu +3
Head generation with diverse identities is an important task in computer vision and computer graphics, widely used in multimedia applications. However, current full head generation…
Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective
Weixia Zhang, Guangtao Zhai, Ying Wei +2
We aim at advancing blind image quality assessment (BIQA), which predicts the human perception of image quality without any reference information. We develop a general and automate…
Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop
Weixia Zhang, Dingquan Li, Xiongkuo Min +4
No-reference image quality assessment (NR-IQA) aims to quantify how humans perceive visual distortions of digital images without access to their undistorted references. NR-IQA mode…
Blind Surveillance Image Quality Assessment via Deep Neural Network Combined with the Visual Saliency
Wei Lu, Wei Sun, Wenhan Zhu +4
The intelligent video surveillance system (IVSS) can automatically analyze the content of the surveillance image (SI) and reduce the burden of the manual labour. However, the SIs m…
Confusing Image Quality Assessment: Towards Better Augmented Reality Experience
Huiyu Duan, Xiongkuo Min, Yucheng Zhu +3
With the development of multimedia technology, Augmented Reality (AR) has become a promising next-generation mobile platform. The primary value of AR is to promote the fusion of di…