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20182022
most citedAdversarial Attacks against Deep Saliency Models

6 citations · 25 across the 10 of their papers we have counts for

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cs.CV2021

Considering user agreement in learning to predict the aesthetic quality

Suiyi Ling, Andreas Pastor, Junle Wang +1

How to robustly rank the aesthetic quality of given images has been a long-standing ill-posed topic. Such challenge stems mainly from the diverse subjective opinions of different o…

cs.CV2021

Subjective and Objective Quality Assessment of Mobile Gaming Video

Shaoguo Wen, Suiyi Ling, Junle Wang +4

Nowadays, with the vigorous expansion and development of gaming video streaming techniques and services, the expectation of users, especially the mobile phone users, for higher qua…

cs.CV2021

Multi-Modal Aesthetic Assessment for MObile Gaming Image

Zhenyu Lei, Yejing Xie, Suiyi Ling +3

With the proliferation of various gaming technology, services, game styles, and platforms, multi-dimensional aesthetic assessment of the gaming contents is becoming more and more i…

cs.CV2020

Few-Shot Object Detection in Real Life: Case Study on Auto-Harvest

Kevin Riou, Jingwen Zhu, Suiyi Ling +3

Confinement during COVID-19 has caused serious effects on agriculture all over the world. As one of the efficient solutions, mechanical harvest/auto-harvest that is based on object…

cs.CV20196 cited

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…

cs.CV2018

Prediction of the Influence of Navigation Scan-path on Perceived Quality of Free-Viewpoint Videos

Suiyi Ling, Jesús Gutiérrez, Gu Ke +1

Free-Viewpoint Video (FVV) systems allow the viewers to freely change the viewpoints of the scene. In such systems, view synthesis and compression are the two main sources of artif…