Search Results for author: Kede Ma

Found 35 papers, 19 papers with code

Scanpath Prediction in Panoramic Videos via Expected Code Length Minimization

no code implementations4 May 2023 Mu Li, Kanglong Fan, Kede Ma

Predicting human scanpaths when exploring panoramic videos is a challenging task due to the spherical geometry and the multimodality of the input, and the inherent uncertainty and diversity of the output.

Data Compression Imitation Learning +1

IconShop: Text-Guided Vector Icon Synthesis with Autoregressive Transformers

no code implementations27 Apr 2023 Ronghuan Wu, Wanchao Su, Kede Ma, Jing Liao

More importantly, we demonstrate the flexibility of IconShop with multiple novel icon synthesis tasks, including icon editing, icon interpolation, icon semantic combination, and icon design auto-suggestion.

Text-to-Image Generation Vector Graphics

Learning a Deep Color Difference Metric for Photographic Images

1 code implementation CVPR 2023 Haoyu Chen, Zhihua Wang, Yang Yang, Qilin Sun, Kede Ma

Most well-established and widely used color difference (CD) metrics are handcrafted and subject-calibrated against uniformly colored patches, which do not generalize well to photographic images characterized by natural scene complexities.

Joint Video Multi-Frame Interpolation and Deblurring under Unknown Exposure Time

1 code implementation CVPR 2023 Wei Shang, Dongwei Ren, Yi Yang, Hongzhi Zhang, Kede Ma, WangMeng Zuo

Moreover, on the seemingly implausible x16 interpolation task, our method outperforms existing methods by more than 1. 5 dB in terms of PSNR.

Contrastive Learning Deblurring +2

Hiding Images in Deep Probabilistic Models

no code implementations5 Oct 2022 Haoyu Chen, Linqi Song, Zhenxing Qian, Xinpeng Zhang, Kede Ma

As an instantiation, we adopt a SinGAN, a pyramid of generative adversarial networks (GANs), to learn the patch distribution of one cover image.

Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop

1 code implementation3 Oct 2022 Weixia Zhang, Dingquan Li, Xiongkuo Min, Guangtao Zhai, Guodong Guo, Xiaokang Yang, Kede Ma

No-reference image quality assessment (NR-IQA) aims to quantify how humans perceive visual distortions of digital images without access to their undistorted references.

No-Reference Image Quality Assessment

Image Quality Assessment: Integrating Model-Centric and Data-Centric Approaches

no code implementations29 Jul 2022 Peibei Cao, Dingquan Li, Kede Ma

Learning-based image quality assessment (IQA) has made remarkable progress in the past decade, but nearly all consider the two key components - model and data - in relative isolation.

Image Quality Assessment

A Database for Perceived Quality Assessment of User-Generated VR Videos

no code implementations13 Jun 2022 Yuming Fang, Yiru Yao, Xiangjie Sui, Kede Ma

Virtual reality (VR) videos (typically in the form of 360$^\circ$ videos) have gained increasing attention due to the fast development of VR technologies and the remarkable popularization of consumer-grade 360$^\circ$ cameras and displays.

Saliency Detection

Measuring Perceptual Color Differences of Smartphone Photographs

1 code implementation26 May 2022 Zhihua Wang, Keshuo Xu, Yang Yang, Jianlei Dong, Shuhang Gu, Lihao Xu, Yuming Fang, Kede Ma

Measuring perceptual color differences (CDs) is of great importance in modern smartphone photography.

Steerable Pyramid Transform Enables Robust Left Ventricle Quantification

1 code implementation20 Jan 2022 Xiangyang Zhu, Kede Ma, Wufeng Xue

First, the basis functions of SPT match the anatomical structure of the LV as well as the geometric characteristics of the estimated indices.

Pseudocylindrical Convolutions for Learned Omnidirectional Image Compression

1 code implementation25 Dec 2021 Mu Li, Kede Ma, Jinxing Li, David Zhang

We first describe parametric pseudocylindrical representation as a generalization of common pseudocylindrical map projections.

Image Compression

Image Quality Assessment in the Modern Age

no code implementations19 Oct 2021 Kede Ma, Yuming Fang

This tutorial provides the audience with the basic theories, methodologies, and current progresses of image quality assessment (IQA).

Image Quality Assessment

Locally Adaptive Structure and Texture Similarity for Image Quality Assessment

no code implementations16 Oct 2021 Keyan Ding, Yi Liu, Xueyi Zou, Shiqi Wang, Kede Ma

The latest advances in full-reference image quality assessment (IQA) involve unifying structure and texture similarity based on deep representations.

Image Quality Assessment Image Super-Resolution

Perceptually Optimized Deep High-Dynamic-Range Image Tone Mapping

no code implementations1 Sep 2021 Chenyang Le, Jiebin Yan, Yuming Fang, Kede Ma

We describe a deep high-dynamic-range (HDR) image tone mapping operator that is computationally efficient and perceptually optimized.

Tone Mapping Vocal Bursts Intensity Prediction

Task-Specific Normalization for Continual Learning of Blind Image Quality Models

no code implementations28 Jul 2021 Weixia Zhang, Kede Ma, Guangtao Zhai, Xiaokang Yang

In this paper, we present a simple yet effective continual learning method for BIQA with improved quality prediction accuracy, plasticity-stability trade-off, and task-order/-length robustness.

Blind Image Quality Assessment Continual Learning

Semi-Supervised Deep Ensembles for Blind Image Quality Assessment

1 code implementation26 Jun 2021 Zhihua Wang, Dingquan Li, Kede Ma

Ensemble methods are generally regarded to be better than a single model if the base learners are deemed to be "accurate" and "diverse."

Blind Image Quality Assessment Ensemble Learning

Troubleshooting Blind Image Quality Models in the Wild

no code implementations CVPR 2021 Zhihua Wang, Haotao Wang, Tianlong Chen, Zhangyang Wang, Kede Ma

Recently, the group maximum differentiation competition (gMAD) has been used to improve blind image quality assessment (BIQA) models, with the help of full-reference metrics.

Blind Image Quality Assessment Network Pruning

Exposing Semantic Segmentation Failures via Maximum Discrepancy Competition

1 code implementation27 Feb 2021 Jiebin Yan, Yu Zhong, Yuming Fang, Zhangyang Wang, Kede Ma

A natural question then arises: Does the superior performance on the closed (and frequently re-used) test sets transfer to the open visual world with unconstrained variations?

Semantic Segmentation

Continual Learning for Blind Image Quality Assessment

1 code implementation19 Feb 2021 Weixia Zhang, Dingquan Li, Chao Ma, Guangtao Zhai, Xiaokang Yang, Kede Ma

In this paper, we formulate continual learning for BIQA, where a model learns continually from a stream of IQA datasets, building on what was learned from previously seen data.

Blind Image Quality Assessment Continual Learning

Efficiently Troubleshooting Image Segmentation Models with Human-In-The-Loop

no code implementations1 Jan 2021 Haotao Wang, Tianlong Chen, Zhangyang Wang, Kede Ma

Image segmentation lays the foundation for many high-stakes vision applications such as autonomous driving and medical image analysis.

Autonomous Driving Image Segmentation +1

Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild

1 code implementation28 May 2020 Weixia Zhang, Kede Ma, Guangtao Zhai, Xiaokang Yang

Nevertheless, due to the distributional shift between images simulated in the laboratory and captured in the wild, models trained on databases with synthetic distortions remain particularly weak at handling realistic distortions (and vice versa).

Blind Image Quality Assessment Learning-To-Rank

Perceptual Quality Assessment of Omnidirectional Images as Moving Camera Videos

2 code implementations21 May 2020 Xiangjie Sui, Kede Ma, Yiru Yao, Yuming Fang

We first carry out a psychophysical experiment to investigate the interplay among the VR viewing conditions, the user viewing behaviors, and the perceived quality of 360{\deg} images.

Video Quality Assessment

Comparison of Image Quality Models for Optimization of Image Processing Systems

1 code implementation4 May 2020 Keyan Ding, Kede Ma, Shiqi Wang, Eero P. Simoncelli

The performance of objective image quality assessment (IQA) models has been evaluated primarily by comparing model predictions to human quality judgments.

Deblurring Denoising +2

Image Quality Assessment: Unifying Structure and Texture Similarity

2 code implementations16 Apr 2020 Keyan Ding, Kede Ma, Shiqi Wang, Eero P. Simoncelli

Objective measures of image quality generally operate by comparing pixels of a "degraded" image to those of the original.

Image Quality Assessment Retrieval +1

Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessment

no code implementations8 Mar 2020 Zhihua Wang, Kede Ma

We then seek pairs of images by comparing the baseline model with a set of full-reference IQA methods in gMAD.

Active Learning Blind Image Quality Assessment

I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively

1 code implementation ICLR 2020 Haotao Wang, Tianlong Chen, Zhangyang Wang, Kede Ma

On the other hand, the trained classifiers have traditionally been evaluated on small and fixed sets of test images, which are deemed to be extremely sparsely distributed in the space of all natural images.

Image Classification

Intrinsic Image Popularity Assessment

1 code implementation3 Jul 2019 Keyan Ding, Kede Ma, Shiqi Wang

The goal of research in automatic image popularity assessment (IPA) is to develop computational models that can accurately predict the potential of a social image to go viral on the Internet.

Image popularity prediction

Learning to Blindly Assess Image Quality in the Laboratory and Wild

1 code implementation1 Jul 2019 Weixia Zhang, Kede Ma, Guangtao Zhai, Xiaokang Yang

Computational models for blind image quality assessment (BIQA) are typically trained in well-controlled laboratory environments with limited generalizability to realistically distorted images.

Blind Image Quality Assessment Learning-To-Rank

Efficient and Effective Context-Based Convolutional Entropy Modeling for Image Compression

2 code implementations24 Jun 2019 Mu Li, Kede Ma, Jane You, David Zhang, WangMeng Zuo

For the former, we directly apply a CCN to the binarized representation of an image to compute the Bernoulli distribution of each code for entropy estimation.

Image Compression

dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs

no code implementations13 Apr 2019 Kede Ma, Wentao Liu, Tongliang Liu, Zhou Wang, DaCheng Tao

One of the biggest challenges in learning BIQA models is the conflict between the gigantic image space (which is in the dimension of the number of image pixels) and the extremely limited reliable ground truth data for training.

Blind Image Quality Assessment Learning-To-Rank

Deep Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks

no code implementations5 Dec 2016 Kede Ma, Huan Fu, Tongliang Liu, Zhou Wang, DaCheng Tao

The human visual system excels at detecting local blur of visual images, but the underlying mechanism is not well understood.

Vocal Bursts Intensity Prediction

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