no code implementations • CVPR 2024 • Kun Yuan, Hongbo Liu, Mading Li, Muyi Sun, Ming Sun, Jiachao Gong, Jinhua Hao, Chao Zhou, Yansong Tang
In this paper, we propose a VQA method named PTM-VQA, which leverages PreTrained Models to transfer knowledge from models pretrained on various pre-tasks, enabling benefits for VQA from different aspects.
1 code implementation • ICCV 2023 • Guandu Liu, Yukang Ding, Mading Li, Ming Sun, Xing Wen, Bin Wang
To enlarge RF with contained LUT sizes, we propose a novel Reconstructed Convolution(RC) module, which decouples channel-wise and spatial calculation.
no code implementations • CVPR 2023 • Kai Zhao, Kun Yuan, Ming Sun, Mading Li, Xing Wen
Blind image quality assessment (BIQA) aims to automatically evaluate the perceived quality of a single image, whose performance has been improved by deep learning-based methods in recent years.
1 code implementation • CVPR 2022 • Jiahao Yu, Li Chen, Mingrui Zhang, Mading Li
While several recent works exploit tree-based algorithm to preserve image content better, all of them resort to hand-crafted adjustment rules to optimize the collage tree structure, leading to the failure of fully exploring the structure space of collage tree.
no code implementations • 19 Oct 2021 • Mingrui Zhang, Mading Li, Li Chen, Jiahao Yu
To overcome the lack of training data, we pretrain our deep aesthetic network on a large scale image aesthetic dataset (CPC) for general aesthetic feature extraction and propose an attention fusion module for structural collage feature representation.
2 code implementations • 6 Jul 2018 • Yueyu Hu, Wenhan Yang, Mading Li, Jiaying Liu
With preceding pixels as the context, traditional intra prediction schemes generate linear predictions based on several predefined directions (i. e. modes) for blocks to be encoded.
1 code implementation • 23 Apr 2018 • Xutong Ren, Mading Li, Wen-Huang Cheng, Jiaying Liu
Many low-light enhancement methods ignore intensive noise in original images.
no code implementations • 3 May 2016 • Mading Li, Jiaying Liu, Zhiwei Xiong, Xiaoyan Sun, Zongming Guo
In this paper, we propose a novel multiplanar autoregressive (AR) model to exploit the correlation in cross-dimensional planes of a similar patch group collected in an image, which has long been neglected by previous AR models.