64 citations · 201 across the 13 of their papers we have counts for
13 papers · 1 filter
Progressive Modality Cooperation for Multi-Modality Domain Adaptation
Weichen Zhang, Dong Xu, Jing Zhang +1
In this work, we propose a new generic multi-modality domain adaptation framework called Progressive Modality Cooperation (PMC) to transfer the knowledge learned from the source do…
DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models
Ximing Xing, Chuang Wang, Haitao Zhou +3
We demonstrate that pre-trained text-to-image diffusion models, despite being trained on raster images, possess a remarkable capacity to guide vector sketch synthesis. In this pape…
GLT-T: Global-Local Transformer Voting for 3D Single Object Tracking in Point Clouds
Jiahao Nie, Zhiwei He, Yuxiang Yang +2
Current 3D single object tracking methods are typically based on VoteNet, a 3D region proposal network. Despite the success, using a single seed point feature as the cue for offset…
Towards Explainable 3D Grounded Visual Question Answering: A New Benchmark and Strong Baseline
Lichen Zhao, Daigang Cai, Jing Zhang +6
Recently, 3D vision-and-language tasks have attracted increasing research interest. Compared to other vision-and-language tasks, the 3D visual question answering (VQA) task is less…
Improving RGB-D Point Cloud Registration by Learning Multi-scale Local Linear Transformation
Ziming Wang, Xiaoliang Huo, Zhenghao Chen +3
Point cloud registration aims at estimating the geometric transformation between two point cloud scans, in which point-wise correspondence estimation is the key to its success. In…
Registration of multi-view point sets under the perspective of expectation-maximization
Jihua Zhu, Jing Zhang, Huimin Lu +1
Registration of multi-view point sets is a prerequisite for 3D model reconstruction. To solve this problem, most of previous approaches either partially explore available informati…
Importance Weighted Adversarial Nets for Partial Domain Adaptation
Jing Zhang, Zewei Ding, Wanqing Li +1
This paper proposes an importance weighted adversarial nets-based method for unsupervised domain adaptation, specific for partial domain adaptation where the target domain has less…
Unsupervised Domain Adaptation: A Multi-task Learning-based Method
Jing Zhang, Wanqing Li, Philip Ogunbona
This paper presents a novel multi-task learning-based method for unsupervised domain adaptation. Specifically, the source and target domain classifiers are jointly learned by consi…
Joint Geometrical and Statistical Alignment for Visual Domain Adaptation
Jing Zhang, Wanqing Li, Philip Ogunbona
This paper presents a novel unsupervised domain adaptation method for cross-domain visual recognition. We propose a unified framework that reduces the shift between domains both st…
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
Jing Zhang, Wanqing Li, Philip Ogunbona +1
This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Speci…
RGB-D-based Action Recognition Datasets: A Survey
Jing Zhang, Wanqing Li, Philip O. Ogunbona +2
Human action recognition from RGB-D (Red, Green, Blue and Depth) data has attracted increasing attention since the first work reported in 2010. Over this period, many benchmark dat…
Online Action Recognition based on Incremental Learning of Weighted Covariance Descriptors
Chang Tang, Pichao Wang, Wanqing Li
Different from traditional action recognition based on video segments, online action recognition aims to recognize actions from unsegmented streams of data in a continuous manner.…
Deep Convolutional Neural Networks for Action Recognition Using Depth Map Sequences
Pichao Wang, Wanqing Li, Zhimin Gao +3
Recently, deep learning approach has achieved promising results in various fields of computer vision. In this paper, a new framework called Hierarchical Depth Motion Maps (HDMM) +…