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Blind image quality assessment via vision-language correspondence: A multitask learning perspective
We aim at advancing blind image quality assessment (BIQA), which predicts the human
perception of image quality without any reference information. We develop a general and …
perception of image quality without any reference information. We develop a general and …
A deep learning based no-reference quality assessment model for ugc videos
Quality assessment for User Generated Content (UGC) videos plays an important role in
ensuring the viewing experience of end-users. Previous UGC video quality assessment …
ensuring the viewing experience of end-users. Previous UGC video quality assessment …
Blindly assess quality of in-the-wild videos via quality-aware pre-training and motion perception
Perceptual quality assessment of the videos acquired in the wilds is of vital importance for
quality assurance of video services. The inaccessibility of reference videos with pristine …
quality assurance of video services. The inaccessibility of reference videos with pristine …
Blind quality assessment for in-the-wild images via hierarchical feature fusion and iterative mixed database training
Image quality assessment (IQA) is very important for both end-users and service providers
since a high-quality image can significantly improve the user's quality of experience (QoE) …
since a high-quality image can significantly improve the user's quality of experience (QoE) …
Image desnowing via deep invertible separation
Images taken on snowy days often suffer from severe negative visual effects caused by
snowflakes. The task of removing snowflakes from a snowy image is known as image …
snowflakes. The task of removing snowflakes from a snowy image is known as image …
Towards dimension-enriched underwater image quality assessment
The absorption and scattering of light in the water medium naturally impair the quality of
underwater images, leading to multiple degradation effects including color casts, reduced …
underwater images, leading to multiple degradation effects including color casts, reduced …
Improved continually evolved classifiers for few-shot class-incremental learning
Few-shot class-incremental learning (FSCIL) aims to continually learn new classes using a
few samples while not forgetting the old classes. The scarcity of new training data will …
few samples while not forgetting the old classes. The scarcity of new training data will …
Machine-learning based monitoring of cognitive workload in rescue missions with drones
In search and rescue missions, drone operations are challenging and cognitively
demanding. High levels of cognitive workload can affect rescuers' performance, leading to …
demanding. High levels of cognitive workload can affect rescuers' performance, leading to …
Blind image quality assessment for authentic distortions by intermediary enhancement and iterative training
With the boom of deep neural networks, blind image quality assessment (BIQA) has
achieved great processes. However, the current BIQA metrics are limited when evaluating …
achieved great processes. However, the current BIQA metrics are limited when evaluating …
Forgetting to remember: A scalable incremental learning framework for cross-task blind image quality assessment
Recent years have witnessed the great success of blind image quality assessment (BIQA) in
various task-specific scenarios, which present invariable distortion types and evaluation …
various task-specific scenarios, which present invariable distortion types and evaluation …