186 citations · 801 across the 49 of their papers we have counts for
44 papers · 1 filter
TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment
Chaofeng Chen, Jiadi Mo, Jingwen Hou +5
Image Quality Assessment (IQA) is a fundamental task in computer vision that has witnessed remarkable progress with deep neural networks. Inspired by the characteristics of the hum…
MIPI 2023 Challenge on Nighttime Flare Removal: Methods and Results
Yuekun Dai, Chongyi Li, Shangchen Zhou +6
Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobi…
Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach
Haoning Wu, Erli Zhang, Liang Liao +6
The proliferation of in-the-wild videos has greatly expanded the Video Quality Assessment (VQA) problem. Unlike early definitions that usually focus on limited distortion types, VQ…
Towards Robust Text-Prompted Semantic Criterion for In-the-Wild Video Quality Assessment
Haoning Wu, Liang Liao, Annan Wang +5
The proliferation of videos collected during in-the-wild natural settings has pushed the development of effective Video Quality Assessment (VQA) methodologies. Contemporary supervi…
MIPI 2023 Challenge on RGB+ToF Depth Completion: Methods and Results
Qingpeng Zhu, Wenxiu Sun, Yuekun Dai +20
Depth completion from RGB images and sparse Time-of-Flight (ToF) measurements is an important problem in computer vision and robotics. While traditional methods for depth completio…
Exploring Opinion-unaware Video Quality Assessment with Semantic Affinity Criterion
Haoning Wu, Liang Liao, Jingwen Hou +6
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…
Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives
Haoning Wu, Erli Zhang, Liang Liao +6
The rapid increase in user-generated-content (UGC) videos calls for the development of effective video quality assessment (VQA) algorithms. However, the objective of the UGC-VQA pr…
Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment
Haoning Wu, Chaofeng Chen, Liang Liao +5
The increased resolution of real-world videos presents a dilemma between efficiency and accuracy for deep Video Quality Assessment (VQA). On the one hand, keeping the original reso…
MIPI 2022 Challenge on RGBW Sensor Re-mosaic: Dataset and Report
Qingyu Yang, Guang Yang, Jun Jiang +7
Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobi…
Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality Assessment
Liang Liao, Kangmin Xu, Haoning Wu +4
With the rapid growth of in-the-wild videos taken by non-specialists, blind video quality assessment (VQA) has become a challenging and demanding problem. Although lots of efforts…
FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling
Haoning Wu, Chaofeng Chen, Jingwen Hou +5
Current deep video quality assessment (VQA) methods are usually with high computational costs when evaluating high-resolution videos. This cost hinders them from learning better vi…
DisCoVQA: Temporal Distortion-Content Transformers for Video Quality Assessment
Haoning Wu, Chaofeng Chen, Liang Liao +4
The temporal relationships between frames and their influences on video quality assessment (VQA) are still under-studied in existing works. These relationships lead to two importan…
FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting
Rui Liu, Hanming Deng, Yangyi Huang +6
Transformer, as a strong and flexible architecture for modelling long-range relations, has been widely explored in vision tasks. However, when used in video inpainting that require…
Dual-Camera Super-Resolution with Aligned Attention Modules
Tengfei Wang, Jiaxin Xie, Wenxiu Sun +2
We present a novel approach to reference-based super-resolution (RefSR) with the focus on dual-camera super-resolution (DCSR), which utilizes reference images for high-quality and…
A Categorized Reflection Removal Dataset with Diverse Real-world Scenes
Chenyang Lei, Xuhua Huang, Chenyang Qi +4
Due to the lack of a large-scale reflection removal dataset with diverse real-world scenes, many existing reflection removal methods are trained on synthetic data plus a small amou…
Efficient Regional Memory Network for Video Object Segmentation
Haozhe Xie, Hongxun Yao, Shangchen Zhou +2
Recently, several Space-Time Memory based networks have shown that the object cues (e.g. video frames as well as the segmented object masks) from the past frames are useful for seg…
Decoupled Spatial-Temporal Transformer for Video Inpainting
Rui Liu, Hanming Deng, Yangyi Huang +6
Video inpainting aims to fill the given spatiotemporal holes with realistic appearance but is still a challenging task even with prosperous deep learning approaches. Recent works i…
Deep Animation Video Interpolation in the Wild
Li Siyao, Shiyu Zhao, Weijiang Yu +4
In the animation industry, cartoon videos are usually produced at low frame rate since hand drawing of such frames is costly and time-consuming. Therefore, it is desirable to devel…
Exploiting Raw Images for Real-Scene Super-Resolution
Xiangyu Xu, Yongrui Ma, Wenxiu Sun +1
Super-resolution is a fundamental problem in computer vision which aims to overcome the spatial limitation of camera sensors. While significant progress has been made in single ima…
Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch
Aojun Zhou, Yukun Ma, Junnan Zhu +5
Sparsity in Deep Neural Networks (DNNs) has been widely studied to compress and accelerate the models on resource-constrained environments. It can be generally categorized into uns…
Semi-synthesis: A fast way to produce effective datasets for stereo matching
Ju He, Enyu Zhou, Liusheng Sun +3
Stereo matching is an important problem in computer vision which has drawn tremendous research attention for decades. Recent years, data-driven methods with convolutional neural ne…
Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising
Xiangyu Xu, Muchen Li, Wenxiu Sun +1
Existing denoising methods typically restore clear results by aggregating pixels from the noisy input. Instead of relying on hand-crafted aggregation schemes, we propose to explici…
Enhanced Quadratic Video Interpolation
Yihao Liu, Liangbin Xie, Li Siyao +3
With the prosperity of digital video industry, video frame interpolation has arisen continuous attention in computer vision community and become a new upsurge in industry. Many lea…
Towards Geometry Guided Neural Relighting with Flash Photography
Di Qiu, Jin Zeng, Zhanghan Ke +2
Previous image based relighting methods require capturing multiple images to acquire high frequency lighting effect under different lighting conditions, which needs nontrivial effo…