L
Ling-Yu Duan
Researcher at Peking University
Publications - 277
Citations - 9282
Ling-Yu Duan is an academic researcher from Peking University. The author has contributed to research in topics: Image retrieval & Feature extraction. The author has an hindex of 39, co-authored 269 publications receiving 6210 citations. Previous affiliations of Ling-Yu Duan include Institute for Infocomm Research Singapore.
Papers
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Journal ArticleDOI
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
TL;DR: This work introduces a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames, and investigates a novel one-shot 3D activity recognition problem on this dataset.
Proceedings ArticleDOI
Global Context-Aware Attention LSTM Networks for 3D Action Recognition
TL;DR: This work proposes a new class of LSTM network, Global Context-Aware Attention L STM (GCA-LSTM), for 3D action recognition, which is able to selectively focus on the informative joints in the action sequence with the assistance of global contextual information.
Journal ArticleDOI
Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks
TL;DR: Wang et al. as discussed by the authors proposed a global context-aware attention LSTM for skeleton-based action recognition, which is capable of selectively focusing on the informative joints in each frame by using global context memory cell.
Journal ArticleDOI
Group-Sensitive Triplet Embedding for Vehicle Reidentification
TL;DR: A deep metric learning method, group-sensitive-triplet embedding (GS-TRE), to recognize and retrieve vehicles, in which intraclass variance is elegantly modeled by incorporating an intermediate representation “group” between samples and each individual vehicle in the triplet network learning.
Proceedings ArticleDOI
VERI-Wild: A Large Dataset and a New Method for Vehicle Re-Identification in the Wild
TL;DR: A new method for vehicle ReID is proposed, in which, the ReID model is coupled into a Feature Distance Adversarial Network (FDA-Net), and a novel feature distance adversary scheme is designed to generate hard negative samples in feature space to facilitate Re ID model training.