activity
20192022
most citedAIM 2020: Scene Relighting and Illumination Estimation Challenge

14 citations · 53 across the 13 of their papers we have counts for

collaborators

16 papers

cs.CV20223 cited

AIM 2022 Challenge on Instagram Filter Removal: Methods and Results

Furkan Kınlı, Sami Menteş, Barış Özcan +30

This paper introduces the methods and the results of AIM 2022 challenge on Instagram Filter Removal. Social media filters transform the images by consecutive non-linear operations,…

cs.LG20213 cited

DPNAS: Neural Architecture Search for Deep Learning with Differential Privacy

Anda Cheng, Jiaxing Wang, Xi Sheryl Zhang +3

Training deep neural networks (DNNs) for meaningful differential privacy (DP) guarantees severely degrades model utility. In this paper, we demonstrate that the architecture of DNN…

cs.CV20213 cited

Improving Binary Neural Networks through Fully Utilizing Latent Weights

Weixiang Xu, Qiang Chen, Xiangyu He +2

Binary Neural Networks (BNNs) rely on a real-valued auxiliary variable W to help binary training. However, pioneering binary works only use W to accumulate gradient updates during…

cs.CV20211 cited

IntraLoss: Further Margin via Gradient-Enhancing Term for Deep Face Recognition

Chengzhi Jiang, Yanzhou Su, Wen Wang +3

Existing classification-based face recognition methods have achieved remarkable progress, introducing large margin into hypersphere manifold to learn discriminative facial represen…

cs.CV20217 cited

You Only Look One-level Feature

Qiang Chen, Yingming Wang, Tong Yang +3

This paper revisits feature pyramids networks (FPN) for one-stage detectors and points out that the success of FPN is due to its divide-and-conquer solution to the optimization pro…

cs.LG2020

Multi-Faceted Representation Learning with Hybrid Architecture for Time Series Classification

Zhenyu Liu, Jian Cheng

Time series classification problems exist in many fields and have been explored for a couple of decades. However, they still remain challenging, and their solutions need to be furt…