activity
20172023
most citedDeep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems

367 citations · 452 across the 25 of their papers we have counts for

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Showing cs.CVShow all

6 papers · 1 filter

cs.CV2020

New Ideas and Trends in Deep Multimodal Content Understanding: A Review

Wei Chen, Weiping Wang, Li Liu +1

The focus of this survey is on the analysis of two modalities of multimodal deep learning: image and text. Unlike classic reviews of deep learning where monomodal image classifiers…

cs.CV2020

On the Exploration of Incremental Learning for Fine-grained Image Retrieval

Wei Chen, Yu Liu, Weiping Wang +3

In this paper, we consider the problem of fine-grained image retrieval in an incremental setting, when new categories are added over time. On the one hand, repeatedly training the…

cs.CV2020

Hyperspectral Unmixing via Nonnegative Matrix Factorization with Handcrafted and Learnt Priors

Min Zhao, Tiande Gao, Jie Chen +1

Nowadays, nonnegative matrix factorization (NMF) based methods have been widely applied to blind spectral unmixing. Introducing proper regularizers to NMF is crucial for mathematic…

cs.CV20205 cited

Global Context Aware Convolutions for 3D Point Cloud Understanding

Zhiyuan Zhang, Binh-Son Hua, Wei Chen +2

Recent advances in deep learning for 3D point clouds have shown great promises in scene understanding tasks thanks to the introduction of convolution operators to consume 3D point…

cs.CV20206 cited

Dual Gaussian-based Variational Subspace Disentanglement for Visible-Infrared Person Re-Identification

Nan Pu, Wei Chen, Yu Liu +2

Visible-infrared person re-identification (VI-ReID) is a challenging and essential task in night-time intelligent surveillance systems. Except for the intra-modality variance that…

cs.CV2020

G2L-Net: Global to Local Network for Real-time 6D Pose Estimation with Embedding Vector Features

Wei Chen, Xi Jia, Hyung Jin Chang +2

In this paper, we propose a novel real-time 6D object pose estimation framework, named G2L-Net. Our network operates on point clouds from RGB-D detection in a divide-and-conquer fa…