AMN: Attention Metric Network for One-Shot Remote Sensing Image Scene Classification

Li, Xirong; Pu, Fangling; Yang, Rui; Gui, Rong; Xu, Xin

doi:10.3390/rs12244046

Cited by 25 publications

(6 citation statements)

References 43 publications

(69 reference statements)

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“…The first Metric-Learning method created to address FSL issues was Matching Networks (Chen et al, 2021). When using the Matching Networks approach to resolve an FSL job, a large base dataset is required (Li X. et al, 2020). This dataset is divided into episodes.…”

Section: Types Of Network In Few-shot Learningmentioning

confidence: 99%

“…FSL is widely used for scene classification (Alajaji et al, 2020), text classification (Muthukumar, 2021), image classification (Li X. et al, 2020) and image retrieval (Zhong Q. et al, 2020). Even though software and computational technologies have long been utilized for detection in agriculture, FSL's capacity to identify them with limited training data makes them suitable for the task.…”

mentioning

confidence: 99%

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Object detection and classification using few-shot learning in smart agriculture: A scoping mini review

Ragu

Teo²

2023

Front. Sustain. Food Syst.

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Smart agriculture is the application of modern information and communication technologies (ICT) to agriculture, leading to what we might call a third green revolution. These include object detection and classification such as plants, leaves, weeds, fruits as well as animals and pests in the agricultural domain. Object detection, one of the most fundamental and difficult issues in computer vision has attracted a lot of attention lately. Its evolution over the previous two decades can be seen as the pinnacle of computer vision advancement. The detection of objects can be done via digital image processing. Machine learning has achieved significant advances in the field of digital image processing in current years, significantly outperforming previous techniques. One of the techniques that is popular is Few-Shot Learning (FSL). FSL is a type of meta-learning in which a learner is given practice on several related tasks during the meta-training phase to be able to generalize successfully to new but related activities with a limited number of instances during the meta-testing phase. Here, the application of FSL in smart agriculture, with particular in the detection and classification is reported. The aim is to review the state of the art of currently available FSL models, networks, classifications, and offer some insights into possible future avenues of research. It is found that FSL shows a higher accuracy of 99.48% in vegetable disease recognition on a limited dataset. It is also shown that FSL is reliable to use with very few instances and less training time.

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Section: Types Of Network In Few-shot Learningmentioning

confidence: 99%

mentioning

confidence: 99%

Object detection and classification using few-shot learning in smart agriculture: A scoping mini review

Ragu

Teo²

2023

Front. Sustain. Food Syst.

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“…Existing few-shot learning methods can be broadly categorized as either: metric learning [23,21,5,34,33,14,15,11], meta-learning [28,17,3,7], or data augmentation [6,27]. Metric learning methods train networks to predict whether two images/regions belong to the same category.…”

Section: Few-shot Learningmentioning

confidence: 99%

Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection

Li¹,

Yang²,

Liu³

et al. 2021

Preprint

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Few-shot object detection has made substantial progress by representing novel class objects using the feature representation learned upon a set of base class objects. However, an implicit contradiction between novel class classification and representation is unfortunately ignored. On the one hand, to achieve accurate novel class classification, the distributions of either two base classes must be far away from each other (max-margin). On the other hand, to precisely represent novel classes, the distributions of base classes should be close to each other to reduce the intra-class distance of novel classes (min-margin). In this paper, we propose a class margin equilibrium (CME) approach, with the aim to optimize both feature space partition and novel class reconstruction in a systematic way. CME first converts the few-shot detection problem to the few-shot classification problem by using a fully connected layer to decouple localization features. CME then reserves adequate margin space for novel classes by introducing simple-yet-effective class margin loss during feature learning. Finally, CME pursues margin equilibrium by disturbing the features of novel class instances in an adversarial min-max fashion. Experiments on Pascal VOC and MS-COCO datasets show that CME significantly improves upon two baseline detectors (up to 3 ∼ 5% in average), achieving state-of-the-art performance. Code is available at https://github.com/Bohao-Lee/CME.

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“…C HANGE detection (CD) reports the temporal dynamics of the studied area by observing it at different times [1]. In geoscience, the observation is conducted through the remote sensing technique [2], [3]. With the information interpreted from multi-temporal satellite images, CD benefits applications such as urban planning [4], environment monitoring [5]- [7], disaster assessment [8]- [11] and resource management [12].…”

Section: Introductionmentioning

confidence: 99%

RDP-Net: Region Detail Preserving Network for Change Detection

Chen¹,

Pu²,

Yang³

et al. 2022

Preprint

Self Cite

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Change detection (CD) is an essential earth observation technique. It captures the dynamic information of land objects. With the rise of deep learning, neural networks (NN) have shown great potential in CD. However, current NN models introduce backbone architectures that lose the detail information during learning. Moreover, current NN models are heavy in parameters, which prevents their deployment on edge devices such as drones. In this work, we tackle this issue by proposing RDP-Net: a region detail preserving network for CD.We propose an efficient training strategy that quantifies the importance of individual samples during the warmup period of NN training. Then, we perform non-uniform sampling based on the importance score so that the NN could learn detail information from easy to hard. Next, we propose an effective edge loss that improves the network's attention on details such as boundaries and small regions. As a result, we provide a NN model that achieves the state-of-the-art empirical performance in CD with only 1.70M parameters. We hope our RDP-Net would benefit the practical CD applications on compact devices and could inspire more people to bring change detection to a new level with the efficient training strategy.

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AMN: Attention Metric Network for One-Shot Remote Sensing Image Scene Classification

Cited by 25 publications

References 43 publications

Object detection and classification using few-shot learning in smart agriculture: A scoping mini review

Object detection and classification using few-shot learning in smart agriculture: A scoping mini review

Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection

RDP-Net: Region Detail Preserving Network for Change Detection

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