155 citations · 258 across the 11 of their papers we have counts for
11 papers
Pose-Free Neural Radiance Fields via Implicit Pose Regularization
Jiahui Zhang, Fangneng Zhan, Yingchen Yu +5
Pose-free neural radiance fields (NeRF) aim to train NeRF with unposed multi-view images and it has achieved very impressive success in recent years. Most existing works share the…
WaveNeRF: Wavelet-based Generalizable Neural Radiance Fields
Muyu Xu, Fangneng Zhan, Jiahui Zhang +5
Neural Radiance Field (NeRF) has shown impressive performance in novel view synthesis via implicit scene representation. However, it usually suffers from poor scalability as requir…
A Survey of Label-Efficient Deep Learning for 3D Point Clouds
Aoran Xiao, Xiaoqin Zhang, Ling Shao +1
In the past decade, deep neural networks have achieved significant progress in point cloud learning. However, collecting large-scale precisely-annotated training data is extremely…
Audio-Driven Talking Face Generation with Diverse yet Realistic Facial Animations
Rongliang Wu, Yingchen Yu, Fangneng Zhan +3
Audio-driven talking face generation, which aims to synthesize talking faces with realistic facial animations (including accurate lip movements, vivid facial expression details and…
Latent Multi-Relation Reasoning for GAN-Prior based Image Super-Resolution
Jiahui Zhang, Fangneng Zhan, Yingchen Yu +3
Recently, single image super-resolution (SR) under large scaling factors has witnessed impressive progress by introducing pre-trained generative adversarial networks (GANs) as prio…
VMRF: View Matching Neural Radiance Fields
Jiahui Zhang, Fangneng Zhan, Rongliang Wu +5
Neural Radiance Fields (NeRF) have demonstrated very impressive performance in novel view synthesis via implicitly modelling 3D representations from multi-view 2D images. However,…
UniDAformer: Unified Domain Adaptive Panoptic Segmentation Transformer via Hierarchical Mask Calibration
Jingyi Zhang, Jiaxing Huang, Xiaoqin Zhang +1
Domain adaptive panoptic segmentation aims to mitigate data annotation challenge by leveraging off-the-shelf annotated data in one or multiple related source domains. However, exis…
DcnnGrasp: Towards Accurate Grasp Pattern Recognition with Adaptive Regularizer Learning
Xiaoqin Zhang, Ziwei Huang, Jingjing Zheng +2
The task of grasp pattern recognition aims to derive the applicable grasp types of an object according to the visual information. Current state-of-the-art methods ignore category i…
Infrared and Visible Image Fusion via Interactive Compensatory Attention Adversarial Learning
Zhishe Wang, Wenyu Shao, Yanlin Chen +2
The existing generative adversarial fusion methods generally concatenate source images and extract local features through convolution operation, without considering their global ch…
Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey
Aoran Xiao, Jiaxing Huang, Dayan Guan +3
Point cloud data have been widely explored due to its superior accuracy and robustness under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved very i…
Referring Segmentation in Images and Videos with Cross-Modal Self-Attention Network
Linwei Ye, Mrigank Rochan, Zhi Liu +2
We consider the problem of referring segmentation in images and videos with natural language. Given an input image (or video) and a referring expression, the goal is to segment the…