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20162023
most citedLocal-Global Temporal Difference Learning for Satellite Video Super-Resolution

176 citations · 214 across the 4 of their papers we have counts for

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7 papers · 1 filter

cs.CV2023★ 2 cited

TransRef: Multi-Scale Reference Embedding Transformer for Reference-Guided Image Inpainting

Taorong Liu, Liang Liao, Delin Chen +4

Image inpainting for completing complicated semantic environments and diverse hole patterns of corrupted images is challenging even for state-of-the-art learning-based inpainting m…

cs.CV2023★ 176 cited

Local-Global Temporal Difference Learning for Satellite Video Super-Resolution

Yi Xiao, Qiangqiang Yuan, Kui Jiang +4

Optical-flow-based and kernel-based approaches have been extensively explored for temporal compensation in satellite Video Super-Resolution (VSR). However, these techniques are les…

cs.CV2022★ 36 cited

Unsupervised Foggy Scene Understanding via Self Spatial-Temporal Label Diffusion

Liang Liao, Wenyi Chen, Jing Xiao +3

Understanding foggy image sequence in the driving scenes is critical for autonomous driving, but it remains a challenging task due to the difficulty in collecting and annotating re…

cs.CV2022

Pruning Networks with Cross-Layer Ranking & k-Reciprocal Nearest Filters

Mingbao Lin, Liujuan Cao, Yuxin Zhang +3

This paper focuses on filter-level network pruning. A novel pruning method, termed CLR-RNF, is proposed. We first reveal a "long-tail" long-tail pruning problem in magnitude-based…

cs.CV2018

SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination

Chih-Chung Hsu, Chia-Wen Lin, Weng-Tai Su +1

Despite generative adversarial networks (GANs) can hallucinate photo-realistic high-resolution (HR) faces from low-resolution (LR) faces, they cannot guarantee preserving the ident…

cs.CV2017

CNN-Based Joint Clustering and Representation Learning with Feature Drift Compensation for Large-Scale Image Data

Chih-Chung Hsu, Chia-Wen Lin

Given a large unlabeled set of images, how to efficiently and effectively group them into clusters based on extracted visual representations remains a challenging problem. To addre…