6 citations · 25 across the 6 of their papers we have counts for
6 papers
Strategy for Boosting Pair Comparison and Improving Quality Assessment Accuracy
Suiyi Ling, Jing Li, Anne Flore Perrin +3
The development of rigorous quality assessment model relies on the collection of reliable subjective data, where the perceived quality of visual multimedia is rated by the human ob…
GPM: A Generic Probabilistic Model to Recover Annotator's Behavior and Ground Truth Labeling
Jing Li, Suiyi Ling, Junle Wang +2
In the big data era, data labeling can be obtained through crowdsourcing. Nevertheless, the obtained labels are generally noisy, unreliable or even adversarial. In this paper, we p…
A New Ensemble Adversarial Attack Powered by Long-term Gradient Memories
Zhaohui Che, Ali Borji, Guangtao Zhai +3
Deep neural networks are vulnerable to adversarial attacks.
Adversarial Attacks against Deep Saliency Models
Zhaohui Che, Ali Borji, Guangtao Zhai +3
Currently, a plethora of saliency models based on deep neural networks have led great breakthroughs in many complex high-level vision tasks (e.g. scene description, object detectio…
Quality Assessment of Free-viewpoint Videos by Quantifying the Elastic Changes of Multi-Scale Motion Trajectories
Suiyi Ling, Jing Li, Zhaohui Che +3
Virtual viewpoints synthesis is an essential process for many immersive applications including Free-viewpoint TV (FTV). A widely used technique for viewpoints synthesis is Depth-Im…
GANs-NQM: A Generative Adversarial Networks based No Reference Quality Assessment Metric for RGB-D Synthesized Views
Suiyi Ling, Jing Li, Junle Wang +1
In this paper, we proposed a no-reference (NR) quality metric for RGB plus image-depth (RGB-D) synthesis images based on Generative Adversarial Networks (GANs), namely GANs-NQM. Du…