7 citations · 8 across the 10 of their papers we have counts for
11 papers
Blind Quality Enhancement of Compressed Video via Fine-Grained Degradation-Guided Sequential Inference
Li Yu, Yingbo Zhao, Shiyu Wu +3
Existing studies on quality enhancement for compressed video (QECV) predominantly rely on known quantization parameters (QPs), training separate enhancement models for each QP sett…
FANeRV: Frequency Separation and Augmentation based Neural Representation for Video
Li Yu, Zhihui Li, Chao Yao +2
Neural representations for video (NeRV) have gained considerable attention for their strong performance across various video tasks. However, existing NeRV methods often struggle to…
Text-Audio-Visual-conditioned Diffusion Model for Video Saliency Prediction
Li Yu, Xuanzhe Sun, Wei Zhou +1
Video saliency prediction is crucial for downstream applications, such as video compression and human-computer interaction. With the flourishing of multimodal learning, researchers…
DVLTA-VQA: Decoupled Vision-Language Modeling with Text-Guided Adaptation for Blind Video Quality Assessment
Li Yu, Situo Wang, Wei Zhou +1
Inspired by the dual-stream theory of the human visual system (HVS) - where the ventral stream is responsible for object recognition and detail analysis, while the dorsal stream fo…
Relevance-guided Audio Visual Fusion for Video Saliency Prediction
Li Yu, Xuanzhe Sun, Pan Gao +1
Audio data, often synchronized with video frames, plays a crucial role in guiding the audience's visual attention. Incorporating audio information into video saliency prediction ta…
Multi-task Feature Enhancement Network for No-Reference Image Quality Assessment
Li Yu
Due to the scarcity of labeled samples in Image Quality Assessment (IQA) datasets, numerous recent studies have proposed multi-task based strategies, which explore feature informat…
High-Frequency Enhanced Hybrid Neural Representation for Video Compression
Li Yu, Zhihui Li, Jimin Xiao +1
Neural Representations for Videos (NeRV) have simplified the video codec process and achieved swift decoding speeds by encoding video content into a neural network, presenting a pr…
Unsupervised Anomaly Detection via Masked Diffusion Posterior Sampling
Di Wu, Shicai Fan, Xue Zhou +4
Reconstruction-based methods have been commonly used for unsupervised anomaly detection, in which a normal image is reconstructed and compared with the given test image to detect a…
Joint End-to-End Image Compression and Denoising: Leveraging Contrastive Learning and Multi-Scale Self-ONNs
Yuxin Xie, Li Yu, Farhad Pakdaman +1
Noisy images are a challenge to image compression algorithms due to the inherent difficulty of compressing noise. As noise cannot easily be discerned from image details, such as hi…
Panoramic Image Inpainting With Gated Convolution And Contextual Reconstruction Loss
Li Yu, Yanjun Gao, Farhad Pakdaman +1
Deep learning-based methods have demonstrated encouraging results in tackling the task of panoramic image inpainting. However, it is challenging for existing methods to distinguish…
Boosting Facial Action Unit Detection Through Jointly Learning Facial Landmark Detection and Domain Separation and Reconstruction
Ziqiao Shang, Li Yu
Recently how to introduce large amounts of unlabeled facial images in the wild into supervised Facial Action Unit (AU) detection frameworks has become a challenging problem. In thi…