Related references
Note: Only part of the references are listed.
Article
Geochemistry & Geophysics
Jiaming Han et al.
Summary: Significant progress has been made in the past decade on detecting objects in aerial images. We propose a single-shot alignment network (S(2)A-Net) that consists of two modules to address the misalignment issue between anchors and convolutional features, improving the consistency between classification score and localization accuracy.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Jia-Xin Wang et al.
Summary: The article introduces a semi-supervised remote sensing image semantic segmentation method, RanPaste, which combines labeled and unlabeled images to enhance segmentation performance. By combining consistency regularization and pseudo label, and utilizing thresholds to gradually improve model performance, the method enables the model to learn more underlying information from unlabeled data.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Artificial Intelligence
Si-Bao Chen et al.
Summary: In this paper, a method for remote sensing image scene classification using a multi-branch local attention network is proposed. This method effectively solves the challenges in remote sensing image classification, enhances the ability of feature representation, and has been proven superior in experiments.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Environmental Sciences
Qinglin Li et al.
Summary: This paper proposes a clustering-based method for representation learning of remote-sensing images. It introduces a metric to measure the discriminativeness of representations and develops an algorithm to achieve even distribution of samples while preserving their neighborhood relations.
Proceedings Paper
Computer Science, Artificial Intelligence
Jianyuan Guo et al.
Summary: This paper presents Hire-MLP, a simple yet competitive vision MLP architecture via Hierarchical rearrangement, which contains two levels of rearrangements. It achieves better performance and delivers excellent results on various vision tasks.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Ze Liu et al.
Summary: This paper presents techniques for scaling Swin Transformer up to 3 billion parameters and the ability to train with high-resolution images. By increasing the capacity and resolution, Swin Transformer achieves new records on four representative vision benchmarks. Several novel technologies are proposed to address training instability and effectively transfer models from low-resolution to high-resolution. Using these techniques and self-supervised pre-training, a strong 3 billion Swin Transformer model is successfully trained, achieving state-of-the-art accuracy on various benchmarks.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Zhuang Liu et al.
Summary: The development of visual recognition has gone through stages from ConvNets to ViTs and then to hybrid approaches. In this work, the design of a pure ConvNet is reexamined and several key components are discovered, resulting in the construction of the ConvNeXt model series. These models compete with Transformers in terms of accuracy and performance while maintaining the simplicity and efficiency of ConvNets.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Sucheng Ren et al.
Summary: Recent Vision Transformer (ViT) models have achieved promising results in computer vision tasks by modeling long-range dependencies through self-attention. However, these models have a limitation in capturing multi-scale features due to the similar receptive fields in each layer. To address this issue, a novel strategy called shunted self-attention (SSA) is proposed, which allows ViTs to model attention at hybrid scales per layer. By injecting heterogeneous receptive field sizes into tokens, SSA enables the learning of relationships between objects with different sizes while reducing computational cost.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Qiang Chen et al.
Summary: This research proposes the Mix-Former method, which combines depth-wise convolution with local-window self-attention and incorporates bi-directional interactions between different levels. It addresses the issues of limited receptive field and weak modeling capability, and achieves efficient feature mixing.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Xiaoyi Dong et al.
Summary: CSWin Transformer is an efficient and effective Transformer-based backbone for general-purpose vision tasks. It achieves competitive performance by using the Cross-Shaped Window self-attention mechanism, Locally-enhanced Positional Encoding, and a hierarchical structure.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2022)
Proceedings Paper
Geography, Physical
Vladimir Risojevic et al.
Summary: The study investigates the effectiveness of using supervised and self-supervised pre-training models in remote sensing scene classification, and proposes domain-adaptive pre-training combining both methods. Results show that self-supervised pre-training outperforms supervised pre-training, and domain-adaptive pre-training achieves state-of-the-art results in HRRS scene classification.
XXIV ISPRS CONGRESS: IMAGING TODAY, FORESEEING TOMORROW, COMMISSION III
(2022)
Article
Geochemistry & Geophysics
Dong Liang et al.
Summary: In this article, the authors propose a novel training sample generator called DEA-Net for small object detection. The method leverages a sample discriminator and multi-task joint training to improve the performance of the model. Extensive experiments demonstrate that the proposed method achieves state-of-the-art results on aerial datasets.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Jia-Xin Wang et al.
Summary: This letter proposes a semi-supervised segmentation method of remote sensing images based on an iterative contrastive network, which significantly improves the model performance by combining a few labeled images and more unlabeled images.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Alexandros Stergiou et al.
Summary: The article introduces SoftPool, a downsampling method that retains more information in reduced activation maps, leading to improved classification accuracy in CNNs. Experiments demonstrate performance improvements on image and video datasets, with limited computational loads and memory requirements.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Xiangtai Li et al.
Summary: This paper introduces a PointFlow module based on the FPN framework for aerial image segmentation, which generates a sparse affinity map to address the foreground-background imbalance and multiple small object challenges. Experimental results demonstrate that the proposed method is more effective and efficient than general semantic segmentation methods.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
(2021)
Article
Engineering, Electrical & Electronic
Yang Long et al.
Summary: This article discusses how to efficiently prepare a suitable benchmark dataset for remote sensing (RS) image interpretation, presenting general guidances on creating benchmark datasets and providing an example of a Million Aerial Image Dataset. It also addresses challenges and perspectives in RS image annotation to facilitate research in benchmark dataset construction.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Environmental Sciences
Jiaxin Wang et al.
Article
Engineering, Electrical & Electronic
Patrick Helber et al.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2019)
Article
Geochemistry & Geophysics
Gui-Song Xia et al.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2017)
Article
Engineering, Electrical & Electronic
Gong Cheng et al.
PROCEEDINGS OF THE IEEE
(2017)
Article
Geochemistry & Geophysics
Zikun Liu et al.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2016)