We study the problem of object detection in remote sensing images. As a simple but effective feature extractor, Feature Pyramid Network (FPN) has been widely used in several generic vision tasks. However, it still faces some challenges when used for remote sensing object detection, as the objects in remote sensing images usually exhibit variable shapes, orientations, and sizes. To this end, we propose a dedicated object detector based on the FPN architecture to achieve accurate object detection in remote sensing images. Specifically, considering the variable shapes and orientations of remote sensing objects, we first replace the original lateral connections of FPN with Deformable Convolution Lateral Connection Modules (DCLCMs), each of which includes a $3\times 3$ deformable convolution to generate feature maps with deformable receptive fields. Additionally, we further introduce several Attention-based Multi-Level Feature Fusion Modules (A-MLFFMs) to integrate the multi-level outputs of FPN adaptively, further enabling multi-scale object detection. Extensive experimental results on the DIOR dataset demonstrated the state-of-the-art performance achieved by the proposed method, with the highest mean Average Precision (mAP) of 73.6%.

To have a better understanding and usage of Convolution Neural Networks (CNNs), the visualization and interpretation of CNNs has attracted increasing attention in recent years. In particular, several Class Activation Mapping (CAM) methods have been proposed to discover the connection between CNN's decision and image regions. In spite of the reasonable visualization, lack of clear and sufficient theoretical support is the main limitation of these methods. In this paper, we introduce two axioms -- Conservation and Sensitivity -- to the visualization paradigm of the CAM methods. Meanwhile, a dedicated Axiom-based Grad-CAM (XGrad-CAM) is proposed to satisfy these axioms as much as possible. Experiments demonstrate that XGrad-CAM is an enhanced version of Grad-CAM in terms of conservation and sensitivity. It is able to achieve better visualization performance than Grad-CAM, while also be class-discriminative and easy-to-implement compared with Grad-CAM++ and Ablation-CAM. The code is available at https://github.com/Fu0511/XGrad-CAM.