Search Results for author: Aoran Xiao

Found 16 papers, 13 papers with code

Unbiased Subclass Regularization for Semi-Supervised Semantic Segmentation

1 code implementation CVPR 2022 Dayan Guan, Jiaxing Huang, Aoran Xiao, Shijian Lu

We build the balanced subclass distributions by clustering pixels of each original class into multiple subclasses of similar sizes, which provide class-balanced pseudo supervision to regularize the class-biased segmentation.

Semi-Supervised Semantic Segmentation

Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey

1 code implementation28 Feb 2022 Aoran Xiao, Jiaxing Huang, Dayan Guan, Xiaoqin Zhang, Shijian Lu

The convergence of point cloud and DNNs has led to many deep point cloud models, largely trained under the supervision of large-scale and densely-labelled point cloud data.

Autonomous Driving Representation Learning

Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data

1 code implementation NeurIPS 2021 Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu

To this end, we design an innovative historical contrastive learning (HCL) technique that exploits historical source hypothesis to make up for the absence of source data in UMA.

Contrastive Learning Unsupervised Domain Adaptation

Domain Adaptive Video Segmentation via Temporal Consistency Regularization

1 code implementation ICCV 2021 Dayan Guan, Jiaxing Huang, Aoran Xiao, Shijian Lu

This paper presents DA-VSN, a domain adaptive video segmentation network that addresses domain gaps in videos by temporal consistency regularization (TCR) for consecutive frames of target-domain videos.

Unsupervised Domain Adaptation Video Semantic Segmentation

Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation

1 code implementation12 Jul 2021 Aoran Xiao, Jiaxing Huang, Dayan Guan, Fangneng Zhan, Shijian Lu

Extensive experiments show that SynLiDAR provides a high-quality data source for studying 3D transfer and the proposed PCT achieves superior point cloud translation consistently across the three setups.

3D Unsupervised Domain Adaptation Data Augmentation +3

Bi-level Feature Alignment for Versatile Image Translation and Manipulation

2 code implementations7 Jul 2021 Fangneng Zhan, Yingchen Yu, Rongliang Wu, Jiahui Zhang, Kaiwen Cui, Aoran Xiao, Shijian Lu, Chunyan Miao

This paper presents a versatile image translation and manipulation framework that achieves accurate semantic and style guidance in image generation by explicitly building a correspondence.

Image Generation Translation

Semi-Supervised Domain Adaptation via Adaptive and Progressive Feature Alignment

no code implementations5 Jun 2021 Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu

We position the few labeled target samples as references that gauge the similarity between source and target features and guide adaptive inter-domain alignment for learning more similar source features.

Domain Adaptation Image Classification +2

RDA: Robust Domain Adaptation via Fourier Adversarial Attacking

1 code implementation ICCV 2021 Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu

With FAA-generated samples, the training can continue the 'random walk' and drift into an area with a flat loss landscape, leading to more robust domain adaptation.

Unsupervised Domain Adaptation

Category Contrast for Unsupervised Domain Adaptation in Visual Tasks

1 code implementation CVPR 2022 Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu, Ling Shao

In this work, we explore the idea of instance contrastive learning in unsupervised domain adaptation (UDA) and propose a novel Category Contrast technique (CaCo) that introduces semantic priors on top of instance discrimination for visual UDA tasks.

Contrastive Learning Representation Learning +1

MLAN: Multi-Level Adversarial Network for Domain Adaptive Semantic Segmentation

no code implementations24 Mar 2021 Jiaxing Huang, Dayan Guan, Shijian Lu, Aoran Xiao

Recent progresses in domain adaptive semantic segmentation demonstrate the effectiveness of adversarial learning (AL) in unsupervised domain adaptation.

Image-to-Image Translation Semantic Segmentation +2

FSDR: Frequency Space Domain Randomization for Domain Generalization

no code implementations CVPR 2021 Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu

It has been studied widely by domain randomization that transfers source images to different styles in spatial space for learning domain-agnostic features.

Domain Generalization

Cross-View Regularization for Domain Adaptive Panoptic Segmentation

1 code implementation CVPR 2021 Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu

The inter-task regularization exploits the complementary nature of instance segmentation and semantic segmentation and uses it as a constraint for better feature alignment across domains.

Instance Segmentation Panoptic Segmentation

FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud Segmentation

1 code implementation1 Mar 2021 Aoran Xiao, Xiaofei Yang, Shijian Lu, Dayan Guan, Jiaxing Huang

Specifically, we design a residual dense block with multiple receptive fields as a building block in the encoder which preserves detailed information in each modality and learns hierarchical modality-specific and fused features effectively.

3D Semantic Segmentation Point Cloud Segmentation +1

Uncertainty-Aware Unsupervised Domain Adaptation in Object Detection

3 code implementations27 Feb 2021 Dayan Guan, Jiaxing Huang, Aoran Xiao, Shijian Lu, Yanpeng Cao

Specifically, we design an uncertainty metric that assesses the alignment of each sample and adjusts the strength of adversarial learning for well-aligned and poorly-aligned samples adaptively.

object-detection Object Detection +1

Indoor Visual Positioning Aided by CNN-Based Image Retrieval: Training-Free, 3D Modeling-Free

1 code implementation journal 2018 Yujin Chen, Ruizhi Chen, Mengyun Liu, Aoran Xiao, Dewen Wu and Shuheng Zhao

The first one is CNN-based image retrieval phase, CNN features extracted by pre-trained deep convolutional neural networks (DCNNs) from images are utilized to compare the similarity, and the output of this part are the matched images of the target image.

Image Retrieval Indoor Localization +4

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