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20182026
most citedGeo-DefakeHop: High-Performance Geographic Fake Image Detection

11 citations · 27 across the 9 of their papers we have counts for

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6 papers · 1 filter

cs.CV2021

PEDENet: Image Anomaly Localization via Patch Embedding and Density Estimation

Kaitai Zhang, Bin Wang, C. -C. Jay Kuo

A neural network targeting at unsupervised image anomaly localization, called the PEDENet, is proposed in this work. PEDENet contains a patch embedding (PE) network, a density esti…

cs.CV2021★ 11 cited

Geo-DefakeHop: High-Performance Geographic Fake Image Detection

Hong-Shuo Chen, Kaitai Zhang, Shuowen Hu +2

A robust fake satellite image detection method, called Geo-DefakeHop, is proposed in this work. Geo-DefakeHop is developed based on the parallel subspace learning (PSL) methodology…

cs.CV2021★ 5 cited

TGHop: An Explainable, Efficient and Lightweight Method for Texture Generation

Xuejing Lei, Ganning Zhao, Kaitai Zhang +1

An explainable, efficient and lightweight method for texture generation, called TGHop (an acronym of Texture Generation PixelHop), is proposed in this work. Although synthesis of v…

cs.CV2021★ 8 cited

AnomalyHop: An SSL-based Image Anomaly Localization Method

Kaitai Zhang, Bin Wang, Wei Wang +3

An image anomaly localization method based on the successive subspace learning (SSL) framework, called AnomalyHop, is proposed in this work. AnomalyHop consists of three modules: 1…

cs.CV2021★ 2 cited

Dynamic Texture Synthesis by Incorporating Long-range Spatial and Temporal Correlations

Kaitai Zhang, Bin Wang, Hong-Shuo Chen +3

The main challenge of dynamic texture synthesis lies in how to maintain spatial and temporal consistency in synthesized videos. The major drawback of existing dynamic texture synth…

cs.CV2018★ 1 cited

Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation

Ye Wang, Jongmoo Choi, Yueru Chen +5

Unsupervised video object segmentation is a crucial application in video analysis without knowing any prior information about the objects. It becomes tremendously challenging when…