Abstract
Attention mechanism in siamese-based trackers has drawn great attention on visual object tracking. However, we experimentally discover that the feature information after being enhanced by attention models with different styles may be interfered with each other for visual tracking. To address this issue, we propose a Dual Attentional Siamese Network (DASNet) to separate channel attention and spatial attention to alleviate the feature interference. Specifically, we propose a dual attention module to enhance the acquired semantic feature expression ability. Especially in spatial dimension, we introduce spatial pyramid attention on multiscale feature pyramid to capture the long-range interdependencies with different granularity. Moreover, we exploit adaptive decision fusion strategy to integrate the outputs from two parallel tracking-heads along two separate dimensions, channel and spatial, so as to effectively take the advantages of attention models with different styles. Extensive experiments are conducted on publicly challenging benchmarks, where the proposed DASNet achieves new state-of-the-art results, by 0.4299 → 0.4515 and 0.3835 → 0.3934 EAO compared with the strong baseline SiamMask (Wang et al., 2019) on VOT 2016 and VOT 2018. Our source code is available at: https://github.com/jwma0725/DASNet.
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