Fully-convolutional Siamese networks for visual tracking have drawn great attention in balancing tracking accuracy and speed. However, there is still some inherent inaccuracy with advanced trackers, since they only learn a general matching model from large scale datasets by off-line training. This generates the target template without sufficient discriminant information and does not adapt well to the current tracking sequence. In this paper, we introduce the channel attention mechanism into the network to better learn the matching model and, during the online tracking phase, we design an initial matting guidance strategy in which: 1) the superpixel matting algorithm is applied to extract the target foreground in the initial frame, and 2) the matted image with foreground only is fed into the network and fused with the original image feature. Under matting guidance, the fused target template has more details for representation of target appearance and more structural information from superpixels for robust tracking. The experimental results on object tracking benchmark (OTB) show that our approach achieves excellent performance while it also provides real-time tracking speed. INDEX TERMSVisual tracking, siamese network, matching model, superpixel, channel attention.

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Medical image segmentation is a fundamental task in medical image analysis. Dynamic receptive field is very helpful for accurate medical image segmentation, which needs to be further studied and utilized. In this paper, we propose Match Feature U-Net, a novel, symmetric encoder– decoder architecture with dynamic receptive field for medical image segmentation. We modify the Selective Kernel convolution (a module proposed in Selective Kernel Networks) by inserting a newly proposed Match operation, which makes similar features in different convolution branches have corresponding positions, and then we replace the U-Net’s convolution with the redesigned Selective Kernel convolution. This network is a combination of U-Net and improved Selective Kernel convolution. It inherits the advantages of simple structure and low parameter complexity of U-Net, and enhances the efficiency of dynamic receptive field in Selective Kernel convolution, making it an ideal model for medical image segmentation tasks which often have small training data and large changes in targets size. Compared with state-of-the-art segmentation methods, the number of parameters in Match Feature U-Net (2.65 M) is 34% of U-Net (7.76 M), 29% of UNet++ (9.04 M), and 9.1% of CE-Net (29 M). We evaluated the proposed architecture in four medical image segmentation tasks: nuclei segmentation in microscopy images, breast cancer cell segmentation, gland segmentation in colon histology images, and disc/cup segmentation. Our experimental results show that Match Feature U-Net achieves an average Mean Intersection over Union (MIoU) gain of 1.8, 1.45, and 2.82 points over U-Net, UNet++, and CE-Net, respectively.