most citedAdversarial Attacks against Deep Saliency Models

6 citations · 31 across the 9 of their papers we have counts for

collaborators

9 papers

cs.AI20206 cited

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…

cs.AI20204 cited

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…

cs.GR20193 cited

Spectral domain decomposition method for physically-based rendering of photochromic/electrochromic glass windows

Guillaume Gbikpi-Benissan, Patrick Callet, Frederic Magoules

This paper covers the time consuming issues intrinsic to physically-based image rendering algorithms. First, glass materials optical properties were measured on samples of real gla…

cs.GR20193 cited

Spectral Domain Decomposition Method for Natural Lighting and Medieval Glass Rendering

Guillaume Gbikpi-Benissan, Remi Cerise, Patrick Callet +1

In this paper, we use an original ray-tracing domain decomposition method to address image rendering of naturally lighted scenes. This new method allows to particularly analyze ren…

cs.GR2019

Spectral domain decomposition method for physically-based rendering of Royaumont abbey

Guillaume Gbikpi-Benissan, Patrick Callet, Frederic Magoules

In the context of a virtual reconstitution of the destroyed Royaumont abbey church, this paper investigates computer sciences issues intrinsic to the physically-based image renderi…

cs.LG20191 cited

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.