Search Results for author: Pu Jin

Found 8 papers, 4 papers with code

Anomaly Detection in Aerial Videos with Transformers

1 code implementation25 Sep 2022 Pu Jin, Lichao Mou, Gui-Song Xia, Xiao Xiang Zhu

In this paper, we create a new dataset, named DroneAnomaly, for anomaly detection in aerial videos.

Anomaly Detection

FuTH-Net: Fusing Temporal Relations and Holistic Features for Aerial Video Classification

no code implementations22 Sep 2022 Pu Jin, Lichao Mou, Yuansheng Hua, Gui-Song Xia, Xiao Xiang Zhu

Furthermore, the holistic features are refined by the multi-scale temporal relations in a novel fusion module for yielding more discriminative video representations.

Action Recognition

Self-supervised Audiovisual Representation Learning for Remote Sensing Data

1 code implementation2 Aug 2021 Konrad Heidler, Lichao Mou, Di Hu, Pu Jin, Guangyao Li, Chuang Gan, Ji-Rong Wen, Xiao Xiang Zhu

By fine-tuning the models on a number of commonly used remote sensing datasets, we show that our approach outperforms existing pre-training strategies for remote sensing imagery.

Representation Learning Transfer Learning

MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images

1 code implementation7 Apr 2021 Yuansheng Hua, Lichao Mou, Pu Jin, Xiao Xiang Zhu

We conduct experiments with extensive baseline models on both MultiScene-Clean and MultiScene to offer benchmarks for multi-scene recognition in single images and learning from noisy labels for this task, respectively.

Learning with noisy labels Scene Recognition

Instance segmentation of buildings using keypoints

no code implementations6 Jun 2020 Qingyu Li, Lichao Mou, Yuansheng Hua, Yao Sun, Pu Jin, Yilei Shi, Xiao Xiang Zhu

The detected keypoints are subsequently reformulated as a closed polygon, which is the semantic boundary of the building.

Instance Segmentation Semantic Segmentation

Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition

1 code implementation ECCV 2020 Di Hu, Xuhong LI, Lichao Mou, Pu Jin, Dong Chen, Liping Jing, Xiaoxiang Zhu, Dejing Dou

With the help of this dataset, we evaluate three proposed approaches for transferring the sound event knowledge to the aerial scene recognition task in a multimodal learning framework, and show the benefit of exploiting the audio information for the aerial scene recognition.

Scene Recognition

ERA: A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos

no code implementations30 Jan 2020 Lichao Mou, Yuansheng Hua, Pu Jin, Xiao Xiang Zhu

In this paper, we introduce a novel problem of event recognition in unconstrained aerial videos in the remote sensing community and present a large-scale, human-annotated dataset, named ERA (Event Recognition in Aerial videos), consisting of 2, 864 videos each with a label from 25 different classes corresponding to an event unfolding 5 seconds.

AID++: An Updated Version of AID on Scene Classification

no code implementations3 Jun 2018 Pu Jin, Gui-Song Xia, Fan Hu, Qikai Lu, Liangpei Zhang

Aerial image scene classification is a fundamental problem for understanding high-resolution remote sensing images and has become an active research task in the field of remote sensing due to its important role in a wide range of applications.

Aerial Scene Classification Classification +2

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