Few-Shot Object Detection via Context-Aware Aggregation for Remote Sensing Images

Yong, Zhou; Hu, He; Zhao, Jiaqi; Zhu, Hancheng; Yao, Rui; Du, Wenliang

doi:10.1109/lgrs.2022.3171257

Cited by 13 publications

(12 citation statements)

References 15 publications

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“…2) NWPU VHR-10: This experiment is conducted on the Faster R-CNN [41] RepMet [31] FeatReweighting [30] TFA [27] Meta R-CNN [29] P-CNN [37] CAAN [40] Ours Faster R-CNN [41] RepMet [31] FeatReweighting [30] TFA [27] Meta R-CNN [29] P-CNN [37] CAAN [40] Ours…”

Section: Experimental Results 1) Diormentioning

confidence: 99%

“…In Split 1 to Split 4, the method is compared with one object detection approach (Faster R-CNN [41]), four natural-images-based classical FSOD algorithms (RepMet [31], FeatReweighting [30], TFA [27], and Meta R-CNN [29]), and two novel RSIs-based FSOD methods (P-CNN [37] and CAAN [40]). CAAN achieves the best performance in Split 1 and Split 3 at three different shots, while P-CNN shows the state-of-the-art results under other situations.…”

Section: B Comparing Methods 1) Diormentioning

confidence: 99%

“…CAAN achieves the best performance in Split 1 and Split 3 at three different shots, while P-CNN shows the state-of-the-art results under other situations. Note that the results of all the comparing methods are cited from P-CNN [37] and CAAN [40]. In Split 5, the proposed method is compared with the two classical FSOD algorithms for natural images, TFA [27] and FeatReweighting [30], and two FSOD approaches in RSIs, FSODM [35] and PAMS-Det [38].…”

Section: B Comparing Methods 1) Diormentioning

confidence: 99%

“…Oriented feature augmentation (OFA) [39] assembles horizontal and vertical flip to increase the amount of training data to ease the arbitrary direction problem of objects in a dual-pipelines structure. Context-aware aggregation network (CAAN) [40] proposes a context-aware pixel aggregation (CPA) that uses convolution of different sizes to adapt to multi-scale objects, and a context-aware feature aggregation (CFA) to utilize more semantic information with a graph convolution network. However, all these attempts treat the information buried in support images as a vector form and totally give up the context relation, making them unable to adapt to the various appearance changes of objects in RSIs.…”

Section: Few-shot Object Detection In Remote Sensing Imagesmentioning

confidence: 99%

“…Oriented feature augmentation (OFA) [39] solves the arbitrary object direction problem with a novel data augmentation module based on dual pipelines. As the latest published work, context-aware aggregation network (CAAN) [40] proposes two components to help the model adapt to images of various scales and learn to strength context awareness to make better predictions. These works are indeed innovative and inspiring, but most of them still have the following two problems after an overall analysis.…”

Section: Introductionmentioning

confidence: 99%

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Few-Shot Object Detection With Self-Adaptive Global Similarity and Two-Way Foreground Stimulator in Remote Sensing Images

Zhang

Wang

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Few-shot object detection aims to localize and recognize potential objects of interest only by using a few annotated data, and it is beneficial for remote sensing images (RSIs) based applications such as urban monitoring. Previous RSIs-based few-shot object detection works often try to convert the support images from class-agnostic features to class-specific vectors, and then perform feature attention operations on query image features to be tested. However, such methods still face two critical challenges: 1) They ignore the spatial similarity of support-query features, which is indispensable for RSIs detection; 2) They perform the feature attention operation in a unidirectional manner, which means that the learned supportquery relations are asymmetric. In this paper, to address the challenges above, we design a few-shot object detector, which can quickly and accurately generalize to unseen categories with only a small amount of data. The proposed approach contains two components: 1) the self-adaptive global similarity module that preserves the internal context information to calculate the similarity map between the objects in support and query images, and 2) the two-way foreground stimulator module that can apply the similarity map to the detailed embeddings of support and query images at the same time to make full use of support information, further strengthening the foreground objects and weakening the unconcerned samples. Experiments are conducted on DIOR and NWPU VHR-10 datasets and their results demonstrate the superiority of the proposed method compared with several state-of-the-art methods.

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Section: Experimental Results 1) Diormentioning

confidence: 99%

Section: B Comparing Methods 1) Diormentioning

confidence: 99%

Section: B Comparing Methods 1) Diormentioning

confidence: 99%

Section: Few-shot Object Detection In Remote Sensing Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Few-Shot Object Detection With Self-Adaptive Global Similarity and Two-Way Foreground Stimulator in Remote Sensing Images

Zhang

Wang

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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LCE-Net: Local-Aware and Context Enhancement based YOLOv5 for object detection in remote sensing images

Yang

Cui

Wang

et al. 2022

2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML)

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FSOD4RSI: Few-Shot Object Detection for Remote Sensing Images via Features Aggregation and Scale Attention

Gao,

Wu,

Wang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Due to the continuous development of few-shot learning, there have been notable advancements in methods for few-shot object detection in recent years. However, most existing methods in this domain primarily focus on natural images, neglecting the challenges posed by variations in object scales, which are usually encountered in remote sensing images. This paper proposes a new few-shot object detection model designed to handle the issue of object scale variation in remote sensing images. Our developed model has two essential parts: a feature aggregation module (FAM) and a scale-aware attention module (SAM). Considering the few-shot features of remote sensing images, we designed the FAM to improve the support and query features through channel multiplication operations utilizing a feature pyramid network (FPN) and a Transformer encoder. The created FAM better extracts the global features of remote sensing images and enhances the significant feature representation of fewshot remote sensing objects. Additionally, we design the SAM to address the scale variation problems that frequently occur in remote sensing images. By employing multiscale convolutions, the SAM enables the acquisition of contextual features while adapting to objects of varying scales. Extensive experiments were conducted on benchmark datasets, including NWPU VHR-10 and DIOR datasets, and the results show that our model indeed addresses the challenges posed by object scale variation and improves the applicability of few-shot object detection in the remote sensing domain.

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Few-Shot Object Detection via Context-Aware Aggregation for Remote Sensing Images

Cited by 13 publications

References 15 publications

Few-Shot Object Detection With Self-Adaptive Global Similarity and Two-Way Foreground Stimulator in Remote Sensing Images

Few-Shot Object Detection With Self-Adaptive Global Similarity and Two-Way Foreground Stimulator in Remote Sensing Images

LCE-Net: Local-Aware and Context Enhancement based YOLOv5 for object detection in remote sensing images

FSOD4RSI: Few-Shot Object Detection for Remote Sensing Images via Features Aggregation and Scale Attention

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