25 citations · 76 across the 8 of their papers we have counts for
8 papers · 1 filter
Meta-Generating Deep Attentive Metric for Few-shot Classification
Lei Zhang, Fei Zhou, Wei Wei +1
Learning to generate a task-aware base learner proves a promising direction to deal with few-shot learning (FSL) problem. Existing methods mainly focus on generating an embedding m…
Vehicle Re-identification in Aerial Imagery: Dataset and Approach
Peng Wang, Bingliang Jiao, Lu Yang +4
In this work, we construct a large-scale dataset for vehicle re-identification (ReID), which contains 137k images of 13k vehicle instances captured by UAV-mounted cameras. To our k…
Pixel-aware Deep Function-mixture Network for Spectral Super-Resolution
Lei Zhang, Zhiqiang Lang, Peng Wang +4
Spectral super-resolution (SSR) aims at generating a hyperspectral image (HSI) from a given RGB image. Recently, a promising direction for SSR is to learn a complicated mapping fun…
Towards Effective Deep Embedding for Zero-Shot Learning
Lei Zhang, Peng Wang, Lingqiao Liu +4
Zero-shot learning (ZSL) can be formulated as a cross-domain matching problem: after being projected into a joint embedding space, a visual sample will match against all candidate…
Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network
Lei Zhang, Peng Wang, Chunhua Shen +4
Deep neural networks have achieved remarkable success in single image super-resolution (SISR). The computing and memory requirements of these methods have hindered their applicatio…
Accurate Spectral Super-resolution from Single RGB Image Using Multi-scale CNN
Yiqi Yan, Lei Zhang, Jun Li +2
Different from traditional hyperspectral super-resolution approaches that focus on improving the spatial resolution, spectral super-resolution aims at producing a high-resolution h…