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Object detection is a significant and challenging problem in the study area of remote sensing and image analysis. However, most existing methods are easy to miss or incorrectly locate objects due to the various sizes and aspect ratios of objects. In this paper, we propose a novel end-to-end Adaptively Aspect Ratio Multi-Scale Network (A 2 RMNet) to solve this problem. On the one hand, we design a multi-scale feature gate fusion network to adaptively integrate the multi-scale features of objects. This network is composed of gate fusion modules, refine blocks and region proposal networks. On the other hand, an aspect ratio attention network is leveraged to preserve the aspect ratios of objects, which alleviates the excessive shape distortions of objects caused by aspect ratio changes during training. Experiments show that the proposed A 2 RMNet significantly outperforms the previous state of the arts on the DOTA dataset, NWPU VHR-10 dataset, RSOD dataset and UCAS-AOD dataset by 5.73 % , 7.06 % , 3.27 % and 2.24 % , respectively. [ABSTRACT FROM AUTHOR]

Person re-identification is an important topic in video surveillance. We present a new feature representation for person re-identification based on multi-region-set ensembles (MRSE) by combining some semantic regions with their contextual information. The motivation of this paper is that people can recognize and identify whether it’s the same person by one or several local regions of the appearance. Our approach is divided into three steps: firstly, we segment the person into some semantic regions such as “hair”, “face”, “up-cloth” (upper clothes) and “lo-cloth” (lower clothes). After getting these regions, we form multiple sets of different combination by selecting a few regions and concatenating the features of them. We then combine the distance of all multiple sets for computing the similarity of a query-gallery image pair. Finally, we achieve higher rank-1 recognition rate and competitive performance compared with the state-of-the-art on iLIDS, VIPeR, CAVIAR4REID, 3DPeS four challenging datasets. [ABSTRACT FROM AUTHOR]