76 citations · 157 across the 14 of their papers we have counts for
14 papers
EEG2MOTION: Towards Open-Vocabulary Human Motion Synthesis from Non-invasive Brain Signals
Yulong Peng, Yijian Pan, Yuqi Yang +4
Human motion is governed by a hierarchical motor system where the brain provides high-level intentions and lower-level structures coordinate detailed dynamics. Existing brain-compu…
Latent Processes Identification From Multi-View Time Series
Zenan Huang, Haobo Wang, Junbo Zhao +1
Understanding the dynamics of time series data typically requires identifying the unique latent factors for data generation, \textit{a.k.a.}, latent processes identification. Drive…
Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmark
Xiaofeng Liu, Jiaxin Gao, Yaohua Liu +2
Recently significant progress has been made in human action recognition and behavior prediction using deep learning techniques, leading to improved vision-based semantic understand…
Discriminative Radial Domain Adaptation
Zenan Huang, Jun Wen, Siheng Chen +2
Domain adaptation methods reduce domain shift typically by learning domain-invariant features. Most existing methods are built on distribution matching, e.g., adversarial domain ad…
SIGMA: A Structural Inconsistency Reducing Graph Matching Algorithm
Weijie Liu, Chao Zhang, Nenggan Zheng +1
Graph matching finds the correspondence of nodes across two correlated graphs and lies at the core of many applications. When graph side information is not available, the node corr…
Approximating Optimal Transport via Low-rank and Sparse Factorization
Weijie Liu, Chao Zhang, Nenggan Zheng +1
Optimal transport (OT) naturally arises in a wide range of machine learning applications but may often become the computational bottleneck. Recently, one line of works propose to s…
From One to All: Learning to Match Heterogeneous and Partially Overlapped Graphs
Weijie Liu, Hui Qian, Chao Zhang +3
Recent years have witnessed a flurry of research activity in graph matching, which aims at finding the correspondence of nodes across two graphs and lies at the heart of many artif…
Interventional Domain Adaptation
Jun Wen, Changjian Shui, Kun Kuang +4
Domain adaptation (DA) aims to transfer discriminative features learned from source domain to target domain. Most of DA methods focus on enhancing feature transferability through d…
Parameter-Efficient Person Re-identification in the 3D Space
Zhedong Zheng, Nenggan Zheng, Yi Yang
People live in a 3D world. However, existing works on person re-identification (re-id) mostly consider the semantic representation learning in a 2D space, intrinsically limiting th…
A Decentralized Proximal Point-type Method for Saddle Point Problems
Weijie Liu, Aryan Mokhtari, Asuman Ozdaglar +3
In this paper, we focus on solving a class of constrained non-convex non-concave saddle point problems in a decentralized manner by a group of nodes in a network. Specifically, we…
Linear Context Transform Block
Dongsheng Ruan, Jun Wen, Nenggan Zheng +1
Squeeze-and-Excitation (SE) block presents a channel attention mechanism for modeling global context via explicitly capturing dependencies across channels. However, we are still fa…
Bayesian Uncertainty Matching for Unsupervised Domain Adaptation
Jun Wen, Nenggan Zheng, Junsong Yuan +2
Domain adaptation is an important technique to alleviate performance degradation caused by domain shift, e.g., when training and test data come from different domains. Most existin…
Exploiting Local Feature Patterns for Unsupervised Domain Adaptation
Jun Wen, Risheng Liu, Nenggan Zheng +3
Unsupervised domain adaptation methods aim to alleviate performance degradation caused by domain-shift by learning domain-invariant representations. Existing deep domain adaptation…
Efficient Spiking Neural Networks with Logarithmic Temporal Coding
Ming Zhang, Nenggan Zheng, De Ma +2
A Spiking Neural Network (SNN) can be trained indirectly by first training an Artificial Neural Network (ANN) with the conventional backpropagation algorithm, then converting it in…