Search Results for author: Yongqiang Mao

Found 13 papers, 4 papers with code

TAFormer: A Unified Target-Aware Transformer for Video and Motion Joint Prediction in Aerial Scenes

no code implementations27 Mar 2024 Liangyu Xu, Wanxuan Lu, Hongfeng Yu, Yongqiang Mao, Hanbo Bi, Chenglong Liu, Xian Sun, Kun fu

To address this issue, we introduce a novel task called Target-Aware Aerial Video Prediction, aiming to simultaneously predict future scenes and motion states of the target.

Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models

no code implementations22 Nov 2023 Xiyu Qi, Yifan Wu, Yongqiang Mao, Wenhui Zhang, Yidan Zhang

The Segment Anything Model (SAM) exhibits remarkable versatility and zero-shot learning abilities, owing largely to its extensive training data (SA-1B).

Few-Shot Semantic Segmentation Segmentation +2

Not Just Learning from Others but Relying on Yourself: A New Perspective on Few-Shot Segmentation in Remote Sensing

1 code implementation19 Oct 2023 Hanbo Bi, Yingchao Feng, Zhiyuan Yan, Yongqiang Mao, Wenhui Diao, Hongqi Wang, Xian Sun

In addition, to prevent the co-existence of multiple classes in remote sensing scenes from exacerbating the collapse of FSS generalization, we also propose a new Known-class Meta Suppressor (KMS) module to suppress the activation of known-class objects in the sample.

Image Segmentation Semantic Segmentation

OGMN: Occlusion-guided Multi-task Network for Object Detection in UAV Images

no code implementations24 Apr 2023 Xuexue Li, Wenhui Diao, Yongqiang Mao, Peng Gao, Xiuhua Mao, Xinming Li, Xian Sun

One interaction for the guide is between two task decoders to address the feature confusion problem, and an occlusion decoupling head (ODH) is proposed to replace the general detection head.

object-detection Object Detection +1

LIGHT: Joint Individual Building Extraction and Height Estimation from Satellite Images through a Unified Multitask Learning Network

no code implementations3 Apr 2023 Yongqiang Mao, Xian Sun, Xingliang Huang, Kaiqiang Chen

Building extraction and height estimation are two important basic tasks in remote sensing image interpretation, which are widely used in urban planning, real-world 3D construction, and other fields.

Instance Segmentation Semantic Segmentation

Elevation Estimation-Driven Building 3D Reconstruction from Single-View Remote Sensing Imagery

no code implementations11 Jan 2023 Yongqiang Mao, Kaiqiang Chen, Liangjin Zhao, Wei Chen, Deke Tang, Wenjie Liu, Zhirui Wang, Wenhui Diao, Xian Sun, Kun fu

Our Building3D is rooted in the SFFDE network for building elevation prediction, synchronized with a building extraction network for building masks, and then sequentially performs point cloud reconstruction, surface reconstruction (or CityGML model reconstruction).

Point cloud reconstruction Surface Reconstruction

Learning to Evaluate Performance of Multi-modal Semantic Localization

1 code implementation14 Sep 2022 Zhiqiang Yuan, Wenkai Zhang, Chongyang Li, Zhaoying Pan, Yongqiang Mao, Jialiang Chen, Shouke Li, Hongqi Wang, Xian Sun

Finally, we analyze the SeLo performance of RS cross-modal retrieval models in detail, explore the impact of different variables on this task, and provide a complete benchmark for the SeLo task.

Cross-Modal Retrieval Referring Expression +2

Bidirectional Feature Globalization for Few-shot Semantic Segmentation of 3D Point Cloud Scenes

no code implementations13 Aug 2022 Yongqiang Mao, Zonghao Guo, Xiaonan Lu, Zhiqiang Yuan, Haowen Guo

With prototype-to-point globalization (Pr2PoG), the global perception is embedded to local point features based on similarity weights from sparse prototypes to dense point features.

Few-Shot Semantic Segmentation Metric Learning +2

Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification

1 code implementation21 Jul 2022 Yongqiang Mao, Kaiqiang Chen, Wenhui Diao, Xian Sun, Xiaonan Lu, Kun fu, Martin Weinmann

With receptive field fusion-and-stratification, RFFS-Net is more adaptable to the classification of regions with complex structures and extreme scale variations in large-scale ALS point clouds.

Classification Point Cloud Classification

Semantic Segmentation for Point Cloud Scenes via Dilated Graph Feature Aggregation and Pyramid Decoders

no code implementations11 Apr 2022 Yongqiang Mao, Xian Sun, Kaiqiang Chen, Wenhui Diao, Zonghao Guo, Xiaonan Lu, Kun fu

Due to the unicity of receptive field, semantic segmentation of point clouds remains challenging for the expression of multi-receptive field features, which brings about the misclassification of instances with similar spatial structures.

Segmentation Semantic Segmentation

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