7 papers
EyeBench: A Call for More Rigorous Evaluation of Retinal Image Enhancement
Wenhui Zhu, Xuanzhao Dong, Xin Li +8
Over the past decade, generative models have achieved significant success in enhancement fundus images.However, the evaluation of these models still presents a considerable challen…
Sequence Complementor: Complementing Transformers For Time Series Forecasting with Learnable Sequences
Xiwen Chen, Peijie Qiu, Wenhui Zhu +5
Since its introduction, the transformer has shifted the development trajectory away from traditional models (e.g., RNN, MLP) in time series forecasting, which is attributed to its…
Multimodal Variational Autoencoder: a Barycentric View
Peijie Qiu, Wenhui Zhu, Sayantan Kumar +6
Multiple signal modalities, such as vision and sounds, are naturally present in real-world phenomena. Recently, there has been growing interest in learning generative models, in pa…
CUNSB-RFIE: Context-aware Unpaired Neural Schrödinger Bridge in Retinal Fundus Image Enhancement
Xuanzhao Dong, Vamsi Krishna Vasa, Wenhui Zhu +7
Retinal fundus photography is significant in diagnosing and monitoring retinal diseases. However, systemic imperfections and operator/patient-related factors can hinder the acquisi…
Context-Aware Optimal Transport Learning for Retinal Fundus Image Enhancement
Vamsi Krishna Vasa, Peijie Qiu, Wenhui Zhu +3
Retinal fundus photography offers a non-invasive way to diagnose and monitor a variety of retinal diseases, but is prone to inherent quality glitches arising from systemic imperfec…
AMG: Avatar Motion Guided Video Generation
Zhangsihao Yang, Mengyi Shan, Mohammad Farazi +4
Human video generation task has gained significant attention with the advancement of deep generative models. Generating realistic videos with human movements is challenging in natu…