most citedActive Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification

169 citations · 197 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CV2019

Shared Predictive Cross-Modal Deep Quantization

Erkun Yang, Cheng Deng, Chao Li +3

With explosive growth of data volume and ever-increasing diversity of data modalities, cross-modal similarity search, which conducts nearest neighbor search across different modali…

cs.CV20191 cited

Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification

Cheng Deng, Xianglong Liu, Chao Li +1

Recent years have witnessed the quick progress of the hyperspectral images (HSI) classification. Most of existing studies either heavily rely on the expensive label information usi…

cs.CV20191 cited

Deep Multi-scale Discriminative Networks for Double JPEG Compression Forensics

Cheng Deng, Zhao Li, Xinbo Gao +1

As JPEG is the most widely used image format, the importance of tampering detection for JPEG images in blind forensics is self-evident. In this area, extracting effective statistic…

cs.CV2019169 cited

Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification

Cheng Deng, Yumeng Xue, Xianglong Liu +2

Deep learning has recently attracted significant attention in the field of hyperspectral images (HSIs) classification. However, the construction of an efficient deep neural network…

cs.IR20191 cited

Triplet-Based Deep Hashing Network for Cross-Modal Retrieval

Cheng Deng, Zhaojia Chen, Xianglong Liu +2

Given the benefits of its low storage requirements and high retrieval efficiency, hashing has recently received increasing attention. In particular,cross-modal hashing has been wid…

cs.CV20191 cited

Semantic Adversarial Network with Multi-scale Pyramid Attention for Video Classification

De Xie, Cheng Deng, Hao Wang +2

Two-stream architecture have shown strong performance in video classification task. The key idea is to learn spatio-temporal features by fusing convolutional networks spatially and…