Search Results for author: Chao Tao

Found 18 papers, 6 papers with code

TOV: The Original Vision Model for Optical Remote Sensing Image Understanding via Self-supervised Learning

no code implementations10 Apr 2022 Chao Tao, Ji Qia, Guo Zhang, Qing Zhu, Weipeng Lu, Haifeng Li

We believe that a general model which is trained by a label-free and task-independent way may be the next paradigm for RSIU and hope the insights distilled from this study can help to foster the development of an original vision model for RSIU.

General Knowledge object-detection +4

Learning by Active Forgetting for Neural Networks

no code implementations21 Nov 2021 Jian Peng, Xian Sun, Min Deng, Chao Tao, Bo Tang, Wenbo Li, Guohua Wu, QingZhu, Yu Liu, Tao Lin, Haifeng Li

This paper presents a learning model by active forgetting mechanism with artificial neural networks.

Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images

1 code implementation20 Jun 2021 Haifeng Li, Yi Li, Guo Zhang, Ruoyun Liu, Haozhe Huang, Qing Zhu, Chao Tao

Supervised learning for semantic segmentation requires a large number of labeled samples, which is difficult to obtain in the field of remote sensing.

Contrastive Learning Self-Supervised Learning +1

Variance-Dependent Best Arm Identification

no code implementations19 Jun 2021 Pinyan Lu, Chao Tao, Xiaojin Zhang

Given a set of $n$ arms indexed from $1$ to $n$, each arm $i$ is associated with an unknown reward distribution supported on $[0, 1]$ with mean $\theta_i$ and variance $\sigma_i^2$.

KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting

1 code implementation26 Nov 2020 Jiawei Zhu, Xin Han, Hanhan Deng, Chao Tao, Ling Zhao, Pu Wang, Lin Tao, Haifeng Li

On this background, this study presents a knowledge representation-driven traffic forecasting method based on spatial-temporal graph convolutional networks.

Knowledge Graphs Representation Learning

Remote Sensing Image Scene Classification with Self-Supervised Paradigm under Limited Labeled Samples

no code implementations2 Oct 2020 Chao Tao, Ji Qi, Weipeng Lu, Hao Wang, Haifeng Li

With the development of deep learning, supervised learning methods perform well in remote sensing images (RSIs) scene classification.

Classification General Classification +2

RS-MetaNet: Deep meta metric learning for few-shot remote sensing scene classification

no code implementations28 Sep 2020 Haifeng Li, Zhenqi Cui, Zhiqing Zhu, Li Chen, Jiawei Zhu, Haozhe Huang, Chao Tao

On the one hand, RS-MetaNet raises the level of learning from the sample to the task by organizing training in a meta way, and it learns to learn a metric space that can well classify remote sensing scenes from a series of tasks.

General Classification Metric Learning +1

Near-Optimal MNL Bandits Under Risk Criteria

no code implementations26 Sep 2020 Guangyu Xi, Chao Tao, Yuan Zhou

We study MNL bandits, which is a variant of the traditional multi-armed bandit problem, under risk criteria.

Spatial Information Considered Network for Scene Classification

1 code implementation IEEE Geoscience and Remote Sensing Letters 2020 Chao Tao, Weipeng Lu, Ji Qi and Hao Wang

Besides, we present an RSI scene classification dataset named as CSU-RSISC10 dataset to preserve the spatial information between scenes in a new way of organization.

Classification General Classification +1

SCAttNet: Semantic Segmentation Network with Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing Images

1 code implementation19 Dec 2019 Haifeng Li, Kaijian Qiu, Li Chen, Xiaoming Mei, Liang Hong, Chao Tao

High-resolution remote sensing images (HRRSIs) contain substantial ground object information, such as texture, shape, and spatial location.

Semantic Segmentation

Spatial Information Inference Net: Road Extraction Using Road-Specific Contextual Information

1 code implementation ISPRS Journal of Photogrammetry and Remote Sensing 2019 Chao Tao, Ji Qi, Yansheng Li, Hao Wang, Haifeng Li

The validation experiments using three large datasets of very high-resolution (VHR) satellite imagery show that the proposed method can improve road extraction accuracy and provide an output that is more in line with human expectations.

Road Segementation

Thresholding Bandit with Optimal Aggregate Regret

no code implementations NeurIPS 2019 Chao Tao, Saùl Blanco, Jian Peng, Yuan Zhou

We consider the thresholding bandit problem, whose goal is to find arms of mean rewards above a given threshold $\theta$, with a fixed budget of $T$ trials.

Collaborative Learning with Limited Interaction: Tight Bounds for Distributed Exploration in Multi-Armed Bandits

no code implementations5 Apr 2019 Chao Tao, Qin Zhang, Yuan Zhou

Best arm identification (or, pure exploration) in multi-armed bandits is a fundamental problem in machine learning.

Multi-Armed Bandits

Best Arm Identification in Linear Bandits with Linear Dimension Dependency

no code implementations ICML 2018 Chao Tao, Saúl Blanco, Yuan Zhou

We study the best arm identification problem in linear bandits, where the mean reward of each arm depends linearly on an unknown $d$-dimensional parameter vector $\theta$, and the goal is to identify the arm with the largest expected reward.

On the Selective and Invariant Representation of DCNN for High-Resolution Remote Sensing Image Recognition

no code implementations4 Aug 2017 Jie Chen, Chao Yuan, Min Deng, Chao Tao, Jian Peng, Haifeng Li

Owing to its superiority in feature representation, DCNN has exhibited remarkable performance in scene recognition of high-resolution remote sensing (HRRS) images and classification of hyper-spectral remote sensing images.

General Classification Scene Recognition

RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data

1 code implementation30 May 2017 Haifeng Li, Xin Dou, Chao Tao, Zhixiang Hou, Jie Chen, Jian Peng, Min Deng, Ling Zhao

In this paper, we propose a remote sensing image classification benchmark (RSI-CB) based on massive, scalable, and diverse crowdsource data.

Classification General Classification +2

What do We Learn by Semantic Scene Understanding for Remote Sensing imagery in CNN framework?

no code implementations19 May 2017 Haifeng Li, Jian Peng, Chao Tao, Jie Chen, Min Deng

Is the DCNN recognition mechanism centered on object recognition still applicable to the scenarios of remote sensing scene understanding?

Object Recognition Scene Recognition +1

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