14 citations · 22 across the 7 of their papers we have counts for
7 papers
Features Based Adaptive Augmentation for Graph Contrastive Learning
Adnan Ali, Jinlong Li
Self-Supervised learning aims to eliminate the need for expensive annotation in graph representation learning, where graph contrastive learning (GCL) is trained with the self-super…
Pik-Fix: Restoring and Colorizing Old Photos
Runsheng Xu, Zhengzhong Tu, Yuanqi Du +6
Restoring and inpainting the visual memories that are present, but often impaired, in old photos remains an intriguing but unsolved research topic. Decades-old photos often suffer…
Exploring High-Order Structure for Robust Graph Structure Learning
Guangqian Yang, Yibing Zhan, Jinlong Li +3
Recent studies show that Graph Neural Networks (GNNs) are vulnerable to adversarial attack, i.e., an imperceptible structure perturbation can fool GNNs to make wrong predictions. S…
ROMNet: Renovate the Old Memories
Runsheng Xu, Zhengzhong Tu, Yuanqi Du +5
Renovating the memories in old photos is an intriguing research topic in computer vision fields. These legacy images often suffer from severe and commingled degradations such as cr…
Let There be Light: Improved Traffic Surveillance via Detail Preserving Night-to-Day Transfer
Lan Fu, Hongkai Yu, Felix Juefei-Xu +3
In recent years, image and video surveillance have made considerable progresses to the Intelligent Transportation Systems (ITS) with the help of deep Convolutional Neural Networks…
Hierarchical Modes Exploring in Generative Adversarial Networks
Mengxiao Hu, Jinlong Li, Maolin Hu +1
In conditional Generative Adversarial Networks (cGANs), when two different initial noises are concatenated with the same conditional information, the distance between their outputs…
Exploring Bias in GAN-based Data Augmentation for Small Samples
Mengxiao Hu, Jinlong Li
For machine learning task, lacking sufficient samples mean the trained model has low confidence to approach the ground truth function. Until recently, after the generative adversar…