With the recent advance of various context aggregation approaches, remarkable progress has been achieved in semantic segmentation. However, it is still challenging to fully exploit the discriminative across-scale context information in an efficient manner. In this paper, we introduce an across-scale context attention network (ACANet) for real-time semantic segmentation. Instead of compute complex query-dependent attention map, we calculate query-independent attention map to aggregate contexts. Experimental results on Cityscape and Camvid datasets demonstrate the effectiveness of our method. In particular, our network achieves 77.4% on the Cityscape test set with a 32 FPS for 1024×2048 images on a single RTX 2080Ti GPU.
Infrared dim and small target detection plays an important role in different applications, and many infrared dim and small target detection methods have been proposed. However, most existing methods are based on single camera. Although they have achieved good performance, most of them have poor performance in complex scenes with dim and small targets. Based on this, we introduce the array camera, which can provide more view information, so as to obtain better performance in the complex scene of dim and small targets. Furthermore, in order to further improve the confidence of detection rate, we introduce a probability estimation module into our method. Specially, the array camera provides more target position information through the view-correlation frames. And the probability estimation module is introduced to fuse the information of each view. Extensive experiments on different scenes demonstrate that our method achieves better performance in dim and small target detection, and obtains higher confidence of detection rate and lower false alarm.
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