Search Results for author: Weixia Zhang

Found 9 papers, 7 papers with code

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

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

1 code implementation19 Aug 2021 Bowen Li, Weixia Zhang, Meng Tian, Guangtao Zhai, Xianpei Wang

The inaccessibility of reference videos with pristine quality and the complexity of authentic distortions pose great challenges for this kind of blind video quality assessment (BVQA) task.

Action Recognition Image Quality Assessment +3

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

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

Language-guided Navigation via Cross-Modal Grounding and Alternate Adversarial Learning

no code implementations22 Nov 2020 Weixia Zhang, Chao Ma, Qi Wu, Xiaokang Yang

We then propose to recursively alternate the learning schemes of imitation and exploration to narrow the discrepancy between training and inference.

Imitation Learning Navigate +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

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

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