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Found 4 papers, 2 papers with code
StarVQA+: Co-training Space-Time Attention for Video Quality Assessment
Self-attention based Transformer has achieved great success in many computer vision tasks.
Object-Attentional Untargeted Adversarial Attack
Specifically, we first generate an object region by intersecting the object detection region from YOLOv4 with the salient object detection (SOD) region from HVPNet.
StarVQA: Space-Time Attention for Video Quality Assessment
To capture the long-range spatiotemporal dependencies of a video sequence, StarVQA encodes the space-time position information of each patch to the input of the Transformer.
Controllable List-wise Ranking for Universal No-reference Image Quality Assessment
First, to extend the authentically distorted image dataset, we present an imaging-heuristic approach, in which the over-underexposure is formulated as an inverse of Weber-Fechner law, and fusion strategy and probabilistic compression are adopted, to generate the degraded real-world images.
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