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- Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network1,013
A deep bilinear model for blind image quality assessment that works for both synthetically and authentically distorted images and achieves state-of-the-art performance on both synthetic and authentic IQA databases is proposed.
- Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels664
The proposed Q-Align achieves state-of-the-art performance on image quality assessment (IQA), image aesthetic assessment (IAA), as well as video quality assessment (VQA) tasks under the original LMM structure and unify the three tasks into one model, termed the OneAlign.
- Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective465
This work develops a general and automated multitask learning scheme for BIQA to exploit auxiliary knowledge from other tasks, in a way that the model parameter sharing and the loss weighting are determined automatically.
- Blindly Assess Quality of In-the-Wild Videos via Quality-Aware Pre-Training and Motion Perception197
This work proposes to transfer knowledge from image quality assessment (IQA) databases with authentic distortions and large-scale action recognition with rich motion patterns and uses a mixed list-wise ranking loss function to train the entire model on the target VQA databases.
- Continual Learning for Blind Image Quality Assessment126
This paper formulates continual learning for BIQA, where a model learns continually from a stream of IQA datasets, building on what was learned from previously seen data, and proposes a simple yet effective continual learning method.
- Language-Guided Navigation via Cross-Modal Grounding and Alternate Adversarial Learning72
A cross-modal grounding module is designed, which is composed of two complementary attention mechanisms, to equip the agent with a better ability to track the correspondence between the textual and visual modalities and further exploit the advantages of both these two learning schemes via adversarial learning.
- Learning To Blindly Assess Image Quality In The Laboratory And Wild62
A BIQA model and an approach of training it on multiple IQA databases (of different distortion scenarios) simultaneously are developed, demonstrating that the optimized model by the proposed training strategy is effective in blindly assessing image quality in the laboratory and wild, outperforming previous BIZA methods by a large margin.
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- AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results13
This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge, focused on User-Generated Content (UGC), to gather deep learning-based methods capable of estimating the perceptual quality of UGC videos.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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