most citedSEMEDA: Enhancing Segmentation Precision with Semantic Edge Aware Loss

2 citations · 2 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV2020

PLOP: Learning without Forgetting for Continual Semantic Segmentation

Arthur Douillard, Yifu Chen, Arnaud Dapogny +1

Deep learning approaches are nowadays ubiquitously used to tackle computer vision tasks such as semantic segmentation, requiring large datasets and substantial computational power.…

cs.CL2019

Guiding Variational Response Generator to Exploit Persona

Bowen Wu, Mengyuan Li, Zongsheng Wang +5

Leveraging persona information of users in Neural Response Generators (NRG) to perform personalized conversations has been considered as an attractive and important topic in the re…

cs.LG2019

REVE: Regularizing Deep Learning with Variational Entropy Bound

Antoine Saporta, Yifu Chen, Michael Blot +1

Studies on generalization performance of machine learning algorithms under the scope of information theory suggest that compressed representations can guarantee good generalization…

cs.CL2019

MemeFaceGenerator: Adversarial Synthesis of Chinese Meme-face from Natural Sentences

Yifu Chen, Zongsheng Wang, Bowen Wu +6

Chinese meme-face is a special kind of internet subculture widely spread in Chinese Social Community Networks. It usually consists of a template image modified by some amusing deta…

cs.CV20192 cited

SEMEDA: Enhancing Segmentation Precision with Semantic Edge Aware Loss

Yifu Chen, Arnaud Dapogny, Matthieu Cord

While nowadays deep neural networks achieve impressive performances on semantic segmentation tasks, they are usually trained by optimizing pixel-wise losses such as cross-entropy.…