Yan Dong

Few-shot object detection aims to accurately identify and localize novel categories using only a small number of labeled samples. In remote sensing images, however, this task faces significant challenges due to substantial variations in target scale and complex backgrounds. To address these issues, this paper proposes a dual-attention guided few-shot object detection framework, DAFSDet. Specifically, a dual-attention strategy is implemented across the feature modeling and proposal generation stages. For feature fusion, the Content-Aware Strip Pyramid (CASP) is designed to enhance multi-scale feature representation by modeling spatial and contextual information. In the detection stage, a Deformable Attention RPN (DA-RPN) is proposed to improve the localization quality of candidate regions. With these designs, the proposed method effectively mitigates the challenges posed by multi-scale variations and complex backgrounds. Experimental results on the DIOR and NWPU VHR-10 datasets demonstrate consistent improvements over baseline methods, with notable gains of 7.54 mAP on DIOR Split 2 under the 10-shot setting and 2.09 mAP on NWPU VHR-10 under the 3-shot setting. These results indicate that the proposed method offers an effective solution for few-shot object detection in complex remote sensing scenarios.

Existing research has demonstrated that meta-learning methods hold considerable promise in addressing the challenges posed by few-shot object detection. However, remote sensing scenarios present two major challenges. The sparse features of small objects provide insufficient support information for query enhancement, and significant morphological variations caused by lighting and viewpoint differences hinder intra-class consistency capture via direct alignment in few-shot learning. To address these challenges, we propose a generative meta-learning detection framework. The framework first introduces a Dynamic Relation Dual-Stream Network to achieve dynamic support-query feature alignment through joint modeling of evolutionary and relational features, thereby enhancing representation in few-shot conditions. Second, an Optimal Transport-based Generative Meta-Learner is developed to mitigate feature distribution bias via generative augmentation in latent space. Additionally, an Orthogonal Frequency Decomposition Head is incorporated to adaptively separate query features into low-frequency contour and high-frequency detail components, effectively suppressing background noise interference. Experiments on multiple remote sensing datasets demonstrate that the proposed method achieves consistent performance gains over leading baseline methods in various few-shot settings. Its effectiveness is further validated across different backbone networks, highlighting strong generalization in few-shot remote sensing object detection.

Small object detection in remote sensing imagery remains challenging due to complex backgrounds, frequent occlusions, and dense distributions of objects, which often lead to suboptimal performance with existing models. To address these issues, this paper proposes a novel dynamic element-activated non-semantic sparse attention method for detecting small objects in remote sensing images. First, we introduce a non-semantic sparse attention mechanism that computes self-attention within local patches, enhancing the model’s focus on textures and edges while improving its perception of occluded small objects and local complex variations. Subsequently, a dynamic element-activated cross-layer channel attention mechanism is incorporated to adaptively strengthen cross-layer positional awareness, thereby specifically enhancing the representational capacity of small objects feature against cluttered backgrounds. Finally, a diffusion wavelet convolutional structure is employed to process multi-channel features in parallel, mitigating information loss and capturing critical features of densely distributed small objects under boundary ambiguity. Extensive ablation studies and comparisons with state-of-the-art methods on the VisDrone and AI-TODv2 datasets demonstrate the feasibility and effectiveness of our approach, showing its potential to provide technical support for practical applications in remote sensing small object detection.

Few-shot object detection focuses on accurately identifying and locating novel categories with just a small set of annotated examples. However, accurate object detection in remote sensing images still faces challenges such as scale variations, small objects, and complex background interferences. To alleviate these issues, we propose DAFSDet, a dual attention guided few-shot object detection framework, to effectively mine discriminative key features. Specifically, we propose a Content-Aware Strip Pyramid (CASP) module that uses content-aware upsampling for spatial attention and bidirectional strip convolution for long-range context, forming a joint spatial–semantic attention mechanism. CASP produces robust multi-scale features, highlighting key regions while preserving semantic and contextual information for few-shot detection. Subsequently, we design a Deformable Attention Region Proposal Network (DA-RPN) to produce high-quality candidate regions from the enhanced features. Through the collaborative optimization of CASP and DA-RPN, our method significantly improves the accuracy and robustness of few-shot object detection in remote sensing images, particularly in complex scenarios with large number of small objects and messy backgrounds. Experimental results on two large-scale datasets, DIOR and NWPU VHR-10, demonstrate that the proposed model achieves strong performance and clear advantages.

Accurate localization of objects of interest in remote sensing images (RSIs) is of great significance for object identification, resource management, decision-making and disaster relief response. However, many difficulties, like complex backgrounds, dense target quantities, large-scale variations, and small-scale objects, which make the detection accuracy unsatisfactory. To improve the detection accuracy, we propose an Adaptive Adjacent Context Negotiation Network (A²CN-Net). Firstly, the composite fast Fourier convolution (CFFC) module is given to reduce the information loss of small objects, which is inserted into the backbone network to obtain spectral global context information. Then, the Global Context Information Enhancement (GCIE) module is given to capture and aggregate global spatial features, which is beneficial for locating objects of different scales. Furthermore, to alleviate the aliasing effect caused by the fusion of adjacent feature layers, a novel Adaptive Adjacent Context Negotiation network (A²CN) is given to adaptive integration of multi-level features, which consists of local and adjacent branches, with the local branch adaptively highlighting feature information and the adjacent branch introducing global information at the adjacent level to enhance feature representation. In the meantime, considering the variability in the focus of feature layers in different dimensions, learnable weights are applied to the local and adjacent branches for adaptive feature fusion. Finally, extensive experiments are performed in several available public datasets, including DIOR and DOTA-v1.0. Experimental studies show that A²CN-Net can significantly boost detection performance, with mAP increasing to 74.2% and 79.2%, respectively.

Accurate localization of objects of interest in remote sensing images (RSIs) is of great significance for object identification, resource management, decision-making and disaster relief response. However, many difficulties, like complex backgrounds, dense target quantities, large-scale variations, and small-scale objects, which make the detection accuracy unsatisfactory. To improve the detection accuracy, we propose an Adaptive Adjacent Context Negotiation Network (A2CN-Net). Firstly, the composite fast Fourier convolution (CFFC) module is given to reduce the information loss of small objects, which is inserted into the backbone network to obtain spectral global context information. Then, the Global Context Information Enhancement (GCIE) module is given to capture and aggregate global spatial features, which is beneficial for locating objects of different scales. Furthermore, to alleviate the aliasing effect caused by the fusion of adjacent feature layers, a novel Adaptive Adjacent Context Negotiation network (A2CN) is given to adaptive integration of multi-level features, which consists of local and adjacent branches, with the local branch adaptively highlighting feature information and the adjacent branch introducing global information at the adjacent level to enhance feature representation. In the meantime, considering the variability in the focus of feature layers in different dimensions, learnable weights are applied to the local and adjacent branches for adaptive feature fusion. Finally, extensive experiments are performed in several available public datasets, including DIOR and DOTA-v1.0. Experimental studies show that A2CN-Net can significantly boost detection performance, with mAP increasing to 74.2% and 79.2%, respectively.

Accurate localization of objects of interest in remote sensing images (RSIs) is of great significance for object identification, resource management, decision-making and disaster relief response. However, many difficulties, like complex backgrounds, dense target quantities, large-scale variations, and small-scale objects, which make the detection accuracy unsatisfactory. To improve the detection accuracy, we propose an Adaptive Adjacent Context Negotiation Network (A2CN-Net). Firstly, the composite fast Fourier convolution (CFFC) module is given to reduce the information loss of small objects, which is inserted into the backbone network to obtain spectral global context information. Then, the Global Context Information Enhancement (GCIE) module is given to capture and aggregate global spatial features, which is beneficial for locating objects of different scales. Furthermore, to alleviate the aliasing effect caused by the fusion of adjacent feature layers, a novel Adaptive Adjacent Context Negotiation network (A2CN) is given to adaptive integration of multi-level features, which consists of local and adjacent branches, with the local branch adaptively highlighting feature information and the adjacent branch introducing global information at the adjacent level to enhance feature representation. In the meantime, considering the variability in the focus of feature layers in different dimensions, learnable weights are applied to the local and adjacent branches for adaptive feature fusion. Finally, extensive experiments are performed in several available public datasets, including DIOR and DOTA-v1.0. Experimental studies show that A2CN-Net can significantly boost detection performance, with mAP increasing to 74.2% and 79.2%, respectively.

Accurate localization of objects of interest in remote sensing images (RSIs) is of great significance for object identification, resource management, decision-making and disaster relief response. However, many difficulties, like complex backgrounds, dense target quantities, large-scale variations, and small-scale objects, which make the detection accuracy unsatisfactory. To improve the detection accuracy, we propose an Adaptive Adjacent Context Negotiation Network (A2CN-Net). Firstly, the composite fast Fourier convolution (CFFC) module is given to reduce the information loss of small objects, which is inserted into the backbone network to obtain spectral global context information. Then, the Global Context Information Enhancement (GCIE) module is given to capture and aggregate global spatial features, which is beneficial for locating objects of different scales. Furthermore, to alleviate the aliasing effect caused by the fusion of adjacent feature layers, a novel Adaptive Adjacent Context Negotiation network (A2CN) is given to adaptive integration of multi-level features, which consists of local and adjacent branches, with the local branch adaptively highlighting feature information and the adjacent branch introducing global information at the adjacent level to enhance feature representation. In the meantime, considering the variability in the focus of feature layers in different dimensions, learnable weights are applied to the local and adjacent branches for adaptive feature fusion. Finally, extensive experiments are performed in several available public datasets, including DIOR and DOTA-v1.0. Experimental studies show that A2CN-Net can significantly boost detection performance, with mAP increasing to 74.2% and 79.2%, respectively.

Accurate localization of objects of interest in remote sensing images (RSIs) is of great significance for object identification, resource management, decision-making and disaster relief response. However, many difficulties, like complex backgrounds, dense target quantities, large-scale variations, and small-scale objects, which make the detection accuracy unsatisfactory. To improve the detection accuracy, we propose an Adaptive Adjacent Context Negotiation Network (A2CN-Net). Firstly, the composite fast Fourier convolution (CFFC) module is given to reduce the information loss of small objects, which is inserted into the backbone network to obtain spectral global context information. Then, the Global Context Information Enhancement (GCIE) module is given to capture and aggregate global spatial features, which is beneficial for locating objects of different scales. Furthermore, to alleviate the aliasing effect caused by the fusion of adjacent feature layers, a novel Adaptive Adjacent Context Negotiation network (A2CN) is given to adaptive integration of multi-level features, which consists of local and adjacent branches, with the local branch adaptively highlighting feature information and the adjacent branch introducing global information at the adjacent level to enhance feature representation. In the meantime, considering the variability in the focus of feature layers in different dimensions, learnable weights are applied to the local and adjacent branches for adaptive feature fusion. Finally, extensive experiments are performed in several available public datasets, including DIOR and DOTA-v1.0. Experimental studies show that A2CN-Net can significantly boost detection performance, with mAP increasing to 74.2% and 79.2%, respectively.

Accurate localization of objects of interest in remote sensing images (RSIs) is of great significance for object identification, resource management, decision-making and disaster relief response. However, many difficulties, like complex backgrounds, dense target quantities, large-scale variations, and small-scale objects, which make the detection accuracy unsatisfactory. To improve the detection accuracy, we propose an Adaptive Adjacent Context Negotiation Network (A2CN-Net). Firstly, the composite fast Fourier convolution (CFFC) module is given to reduce the information loss of small objects, which is inserted into the backbone network to obtain spectral global context information. Then, the Global Context Information Enhancement (GCIE) module is given to capture and aggregate global spatial features, which is beneficial for locating objects of different scales. Furthermore, to alleviate the aliasing effect caused by the fusion of adjacent feature layers, a novel Adaptive Adjacent Context Negotiation network (A2CN) is given to adaptive integration of multi-level features, which consists of local and adjacent branches, with the local branch adaptively highlighting feature information and the adjacent branch introducing global information at the adjacent level to enhance feature representation. In the meantime, considering the variability in the focus of feature layers in different dimensions, learnable weights are applied to the local and adjacent branches for adaptive feature fusion. Finally, extensive experiments are performed in several available public datasets, including DIOR and DOTA-v1.0. Experimental studies show that A2CN-Net can significantly boost detection performance, with mAP increasing to 74.2% and 79.2%, respectively.