Weixia Zhang

LIQE. [CVPR2023] Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective

DBCNN-PyTorch. An experimental Pytorch implementation of Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network

UNIQUE. The repository for 'Uncertainty-aware blind image quality assessment in the laboratory and wild' and 'Learning to blindly assess image quality in the laboratory and wild'

DBCNN. Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network

BIQA_CL. [TPAMI2023] Continual Learning for Blind Image Quality Assessment

TCSVT-2022-BVQA. Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception

ADTH-QA. Python

PerceptualAttack_BIQA. [NeurIPS2022] Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop

IAA_project. Codebase for image aesthetic assessment

TSN-IQA. [TIP2024] Task-specific normalization for continual learning of blind image quality models

LIQ3A. Python

IQA-PyTorch. 👁️ 🖼️ 🔥PyTorch Toolbox for Image Quality Assessment, including LPIPS, FID, NIQE, NRQM(Ma), MUSIQ, NIMA, DBCNN, WaDIQaM, BRISQUE, PI and more...

vit-pytorch. Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

NTIRE2025-XGC-Track1.

bilinear-cnn. PyTorch implementation of bilinear CNN for fine-grained image recognition

acad-homepage.github.io. AcadHomepage: A Modern and Responsive Academic Personal Homepage