1 code implementation • 29 Apr 2024 • Chunyi Li, HaoNing Wu, Hongkun Hao, ZiCheng Zhang, Tengchaun Kou, Chaofeng Chen, Lei Bai, Xiaohong Liu, Weisi Lin, Guangtao Zhai
Based on the mechanisms of the Human Visual System (HVS) and syntax trees, the first two indicators can respectively identify the perception and alignment deficiencies, and the last module can apply targeted quality enhancement accordingly.
no code implementations • 28 Apr 2024 • ZiCheng Zhang, HaoNing Wu, Yingjie Zhou, Chunyi Li, Wei Sun, Chaofeng Chen, Xiongkuo Min, Xiaohong Liu, Weisi Lin, Guangtao Zhai
Although large multi-modality models (LMMs) have seen extensive exploration and application in various quality assessment studies, their integration into Point Cloud Quality Assessment (PCQA) remains unexplored.
no code implementations • 26 Feb 2024 • HaoNing Wu, Hanwei Zhu, ZiCheng Zhang, Erli Zhang, Chaofeng Chen, Liang Liao, Chunyi Li, Annan Wang, Wenxiu Sun, Qiong Yan, Xiaohong Liu, Guangtao Zhai, Shiqi Wang, Weisi Lin
Comparative settings (e. g. pairwise choice, listwise ranking) have been adopted by a wide range of subjective studies for image quality assessment (IQA), as it inherently standardizes the evaluation criteria across different observers and offer more clear-cut responses.
1 code implementation • 28 Dec 2023 • HaoNing Wu, ZiCheng Zhang, Weixia Zhang, Chaofeng Chen, Liang Liao, Chunyi Li, Yixuan Gao, Annan Wang, Erli Zhang, Wenxiu Sun, Qiong Yan, Xiongkuo Min, Guangtao Zhai, Weisi Lin
The explosion of visual content available online underscores the requirement for an accurate machine assessor to robustly evaluate scores across diverse types of visual contents.
Ranked #1 on Video Quality Assessment on LIVE-FB LSVQ
1 code implementation • 9 Dec 2023 • Chaofeng Chen, Shangchen Zhou, Liang Liao, HaoNing Wu, Wenxiu Sun, Qiong Yan, Weisi Lin
Distortion removal involves simple HQ token prediction with LQ images, while texture generation uses a discrete diffusion model to iteratively refine the distortion removal output with a token refinement network.
1 code implementation • 27 Nov 2023 • Chaofeng Chen, Annan Wang, HaoNing Wu, Liang Liao, Wenxiu Sun, Qiong Yan, Weisi Lin
While fine-tuning the U-Net can partially improve performance, it remains suffering from the suboptimal text encoder.
1 code implementation • 12 Nov 2023 • HaoNing Wu, ZiCheng Zhang, Erli Zhang, Chaofeng Chen, Liang Liao, Annan Wang, Kaixin Xu, Chunyi Li, Jingwen Hou, Guangtao Zhai, Geng Xue, Wenxiu Sun, Qiong Yan, Weisi Lin
Multi-modality foundation models, as represented by GPT-4V, have brought a new paradigm for low-level visual perception and understanding tasks, that can respond to a broad range of natural human instructions in a model.
1 code implementation • 25 Sep 2023 • HaoNing Wu, ZiCheng Zhang, Erli Zhang, Chaofeng Chen, Liang Liao, Annan Wang, Chunyi Li, Wenxiu Sun, Qiong Yan, Guangtao Zhai, Weisi Lin
To address this gap, we present Q-Bench, a holistic benchmark crafted to systematically evaluate potential abilities of MLLMs on three realms: low-level visual perception, low-level visual description, and overall visual quality assessment.
no code implementations • 23 Aug 2023 • Kangmin Xu, Liang Liao, Jing Xiao, Chaofeng Chen, HaoNing Wu, Qiong Yan, Weisi Lin
Further, we propose a local distortion extractor to obtain local distortion features from the pretrained CNN and a local distortion injector to inject the local distortion features into ViT.
1 code implementation • 6 Aug 2023 • Chaofeng Chen, Jiadi Mo, Jingwen Hou, HaoNing Wu, Liang Liao, Wenxiu Sun, Qiong Yan, Weisi Lin
Our approach to IQA involves the design of a heuristic coarse-to-fine network (CFANet) that leverages multi-scale features and progressively propagates multi-level semantic information to low-level representations in a top-down manner.
Ranked #11 on Video Quality Assessment on MSU SR-QA Dataset
no code implementations • 19 Jul 2023 • Xiaohong Liu, Xiongkuo Min, Wei Sun, Yulun Zhang, Kai Zhang, Radu Timofte, Guangtao Zhai, Yixuan Gao, Yuqin Cao, Tengchuan Kou, Yunlong Dong, Ziheng Jia, Yilin Li, Wei Wu, Shuming Hu, Sibin Deng, Pengxiang Xiao, Ying Chen, Kai Li, Kai Zhao, Kun Yuan, Ming Sun, Heng Cong, Hao Wang, Lingzhi Fu, Yusheng Zhang, Rongyu Zhang, Hang Shi, Qihang Xu, Longan Xiao, Zhiliang Ma, Mirko Agarla, Luigi Celona, Claudio Rota, Raimondo Schettini, Zhiwei Huang, Yanan Li, Xiaotao Wang, Lei Lei, Hongye Liu, Wei Hong, Ironhead Chuang, Allen Lin, Drake Guan, Iris Chen, Kae Lou, Willy Huang, Yachun Tasi, Yvonne Kao, Haotian Fan, Fangyuan Kong, Shiqi Zhou, Hao liu, Yu Lai, Shanshan Chen, Wenqi Wang, HaoNing Wu, Chaofeng Chen, Chunzheng Zhu, Zekun Guo, Shiling Zhao, Haibing Yin, Hongkui Wang, Hanene Brachemi Meftah, Sid Ahmed Fezza, Wassim Hamidouche, Olivier Déforges, Tengfei Shi, Azadeh Mansouri, Hossein Motamednia, Amir Hossein Bakhtiari, Ahmad Mahmoudi Aznaveh
61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions.
no code implementations • 18 Jul 2023 • Chaofeng Chen, Wei Liu, Xiao Tan, Kwan-Yee K. Wong
Experiments show that SCG achieves competitive performance on public benchmarks and superior results on photos in the wild.
1 code implementation • 22 May 2023 • HaoNing Wu, Erli Zhang, Liang Liao, Chaofeng Chen, Jingwen Hou, Annan Wang, Wenxiu Sun, Qiong Yan, Weisi Lin
Though subjective studies have collected overall quality scores for these videos, how the abstract quality scores relate with specific factors is still obscure, hindering VQA methods from more concrete quality evaluations (e. g. sharpness of a video).
2 code implementations • 28 Apr 2023 • HaoNing Wu, Liang Liao, Annan Wang, Chaofeng Chen, Jingwen Hou, Wenxiu Sun, Qiong Yan, Weisi Lin
The proliferation of videos collected during in-the-wild natural settings has pushed the development of effective Video Quality Assessment (VQA) methodologies.
2 code implementations • 26 Feb 2023 • HaoNing Wu, Liang Liao, Jingwen Hou, Chaofeng Chen, Erli Zhang, Annan Wang, Wenxiu Sun, Qiong Yan, Weisi Lin
Recent learning-based video quality assessment (VQA) algorithms are expensive to implement due to the cost of data collection of human quality opinions, and are less robust across various scenarios due to the biases of these opinions.
1 code implementation • 9 Dec 2022 • Shuliang Ning, Mengcheng Lan, Yanran Li, Chaofeng Chen, Qian Chen, Xunlai Chen, Xiaoguang Han, Shuguang Cui
The mainstream of the existing approaches for video prediction builds up their models based on a Single-In-Single-Out (SISO) architecture, which takes the current frame as input to predict the next frame in a recursive manner.
3 code implementations • ICCV 2023 • HaoNing Wu, Erli Zhang, Liang Liao, Chaofeng Chen, Jingwen Hou, Annan Wang, Wenxiu Sun, Qiong Yan, Weisi Lin
In light of this, we propose the Disentangled Objective Video Quality Evaluator (DOVER) to learn the quality of UGC videos based on the two perspectives.
Ranked #1 on Video Quality Assessment on LIVE-VQC
no code implementations • 17 Oct 2022 • Wenqi Yang, GuanYing Chen, Chaofeng Chen, Zhenfang Chen, Kwan-Yee K. Wong
Different from existing single-view methods which can only recover a 2. 5D scene representation (i. e., a normal / depth map for the visible surface), our method learns a neural reflectance field to represent the 3D geometry and BRDFs of a scene.
4 code implementations • 11 Oct 2022 • HaoNing Wu, Chaofeng Chen, Liang Liao, Jingwen Hou, Wenxiu Sun, Qiong Yan, Jinwei Gu, Weisi Lin
On the other hand, existing practices, such as resizing and cropping, will change the quality of original videos due to the loss of details and contents, and are therefore harmful to quality assessment.
Ranked #2 on Video Quality Assessment on KoNViD-1k (using extra training data)
1 code implementation • 3 Oct 2022 • Xiaoming Li, Chaofeng Chen, Xianhui Lin, WangMeng Zuo, Lei Zhang
Notably, LQ face images, which may have the same degradation process as natural images, can be robustly restored with photo-realistic textures by exploiting their strong structural priors.
no code implementations • 23 Jul 2022 • Wenqi Yang, GuanYing Chen, Chaofeng Chen, Zhenfang Chen, Kwan-Yee K. Wong
It then jointly optimizes the surface normals, spatially-varying BRDFs, and lights based on a shadow-aware differentiable rendering layer.
1 code implementation • 8 Jul 2022 • Liang Liao, Kangmin Xu, HaoNing Wu, Chaofeng Chen, Wenxiu Sun, Qiong Yan, Weisi Lin
Experiments show that the perceptual representation in the HVS is an effective way of predicting subjective temporal quality, and thus TPQI can, for the first time, achieve comparable performance to the spatial quality metric and be even more effective in assessing videos with large temporal variations.
4 code implementations • 6 Jul 2022 • HaoNing Wu, Chaofeng Chen, Jingwen Hou, Liang Liao, Annan Wang, Wenxiu Sun, Qiong Yan, Weisi Lin
Consisting of fragments and FANet, the proposed FrAgment Sample Transformer for VQA (FAST-VQA) enables efficient end-to-end deep VQA and learns effective video-quality-related representations.
Ranked #3 on Video Quality Assessment on LIVE-VQC (using extra training data)
1 code implementation • 20 Jun 2022 • HaoNing Wu, Chaofeng Chen, Liang Liao, Jingwen Hou, Wenxiu Sun, Qiong Yan, Weisi Lin
Based on prominent time-series modeling ability of transformers, we propose a novel and effective transformer-based VQA method to tackle these two issues.
Ranked #5 on Video Quality Assessment on KoNViD-1k
2 code implementations • 26 Feb 2022 • Chaofeng Chen, Xinyu Shi, Yipeng Qin, Xiaoming Li, Xiaoguang Han, Tao Yang, Shihui Guo
Unlike image-space methods, our FeMaSR restores HR images by matching distorted LR image {\it features} to their distortion-free HR counterparts in our pretrained HR priors, and decoding the matched features to obtain realistic HR images.
1 code implementation • 15 Feb 2022 • Shaozhe Hao, Chaofeng Chen, Zhenfang Chen, Kwan-Yee K. Wong
We introduce rectification blocks to rectify features extracted by a state-of-the-art recognition model, in both spatial and channel dimensions, to minimize the distance between a masked face and its mask-free counterpart in the rectified feature space.
no code implementations • 2 Sep 2021 • Wenqi Yang, Zhenfang Chen, Chaofeng Chen, GuanYing Chen, Kwan-Yee K. Wong
In Stage I, we perform face inpainting in the UV space.
1 code implementation • ICCV 2021 • GuanYing Chen, Chaofeng Chen, Shi Guo, Zhetong Liang, Kwan-Yee K. Wong, Lei Zhang
Secondly, we conduct more sophisticated alignment and temporal fusion in the feature space of the coarse HDR video to produce better reconstruction.
1 code implementation • 2 Dec 2020 • Chaofeng Chen, Dihong Gong, Hao Wang, Zhifeng Li, Kwan-Yee K. Wong
Visualization of the attention maps shows that our spatial attention network can capture the key face structures well even for very low resolution faces (e. g., $16\times16$).
1 code implementation • 18 Sep 2020 • Chaofeng Chen, Xiao Tan, Kwan-Yee K. Wong
We utilize a fully convolutional neural network (FCNN) to create the content image, and propose a style transfer approach to introduce textures and shadings based on a newly proposed pyramid column feature.
1 code implementation • CVPR 2021 • Chaofeng Chen, Xiaoming Li, Lingbo Yang, Xianhui Lin, Lei Zhang, Kwan-Yee K. Wong
Compared with previous networks, the proposed PSFR-GAN makes full use of the semantic (parsing maps) and pixel (LQ images) space information from different scales of input pairs.
Ranked #4 on Blind Face Restoration on CelebA-Test
1 code implementation • ECCV 2020 • Xiaoming Li, Chaofeng Chen, Shangchen Zhou, Xianhui Lin, WangMeng Zuo, Lei Zhang
Next, with the degraded input, we match and select the most similar component features from their corresponding dictionaries and transfer the high-quality details to the input via the proposed dictionary feature transfer (DFT) block.
no code implementations • 17 Jan 2019 • Wei Liu, Chaofeng Chen, Kwan-Yee K. Wong
We propose a novel scale aware feature encoder (SAFE) that is designed specifically for encoding characters with different scales.
1 code implementation • 12 Dec 2018 • Chaofeng Chen, Wei Liu, Xiao Tan, Kwan-Yee K. Wong
Instead of supervising the network with ground truth sketches, we first perform patch matching in feature space between the input photo and photos in a small reference set of photo-sketch pairs.
Ranked #1 on Face Sketch Synthesis on CUHK
no code implementations • AAAI 2018 • Wei Liu, Chaofeng Chen, Kwan-Yee K. Wong
Unlike previous work which employed a global spatial transformer network to rectify the entire distorted text image, we take an approach of detecting and rectifying individual characters.