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

169 citations · 177 across the 5 of their papers we have counts for

collaborators

6 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.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.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…

cs.IR20196 cited

Coupled CycleGAN: Unsupervised Hashing Network for Cross-Modal Retrieval

Chao Li, Cheng Deng, Lei Wang +2

In recent years, hashing has attracted more and more attention owing to its superior capacity of low storage cost and high query efficiency in large-scale cross-modal retrieval. Be…

cs.CV2018

Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval

Chao Li, Cheng Deng, Ning Li +3

Thanks to the success of deep learning, cross-modal retrieval has made significant progress recently. However, there still remains a crucial bottleneck: how to bridge the modality…