85 citations · 123 across the 9 of their papers we have counts for
9 papers
UniMAE: Multi-modal Masked Autoencoders with Unified 3D Representation for 3D Perception in Autonomous Driving
Jian Zou, Tianyu Huang, Guanglei Yang +4
Masked Autoencoders (MAE) play a pivotal role in learning potent representations, delivering outstanding results across various 3D perception tasks essential for autonomous driving…
Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation
Guanglei Yang, Enrico Fini, Dan Xu +5
A fundamental and challenging problem in deep learning is catastrophic forgetting, i.e. the tendency of neural networks to fail to preserve the knowledge acquired from old tasks wh…
Continual Attentive Fusion for Incremental Learning in Semantic Segmentation
Guanglei Yang, Enrico Fini, Dan Xu +5
Over the past years, semantic segmentation, as many other tasks in computer vision, benefited from the progress in deep neural networks, resulting in significantly improved perform…
Global and Local Alignment Networks for Unpaired Image-to-Image Translation
Guanglei Yang, Hao Tang, Humphrey Shi +5
The goal of unpaired image-to-image translation is to produce an output image reflecting the target domain's style while keeping unrelated contents of the input source image unchan…
Bi-Mix: Bidirectional Mixing for Domain Adaptive Nighttime Semantic Segmentation
Guanglei Yang, Zhun Zhong, Hao Tang +3
In autonomous driving, learning a segmentation model that can adapt to various environmental conditions is crucial. In particular, copying with severe illumination changes is an im…
Transformer-Based Source-Free Domain Adaptation
Guanglei Yang, Hao Tang, Zhun Zhong +4
In this paper, we study the task of source-free domain adaptation (SFDA), where the source data are not available during target adaptation. Previous works on SFDA mainly focus on a…
Transformer-Based Attention Networks for Continuous Pixel-Wise Prediction
Guanglei Yang, Hao Tang, Mingli Ding +2
While convolutional neural networks have shown a tremendous impact on various computer vision tasks, they generally demonstrate limitations in explicitly modeling long-range depend…
Variational Structured Attention Networks for Deep Visual Representation Learning
Guanglei Yang, Paolo Rota, Xavier Alameda-Pineda +3
Convolutional neural networks have enabled major progresses in addressing pixel-level prediction tasks such as semantic segmentation, depth estimation, surface normal prediction an…
Bi-Directional Generation for Unsupervised Domain Adaptation
Guanglei Yang, Haifeng Xia, Mingli Ding +1
Unsupervised domain adaptation facilitates the unlabeled target domain relying on well-established source domain information. The conventional methods forcefully reducing the domai…