用于视觉跟踪的多注意力关联预测网络

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neurocomputing Pub Date : 2024-10-28 DOI:10.1016/j.neucom.2024.128785
Xinglong Sun , Haijiang Sun , Shan Jiang , Jiacheng Wang , Xilai Wei , Zhonghe Hu
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引用次数: 0

摘要

分类-回归预测网络在一些现代深度跟踪器中取得了令人瞩目的成功。然而,分类任务和回归任务之间存在着内在差异,因此它们对特征匹配的要求各不相同,甚至截然相反。现有模型总是忽略这一关键问题,只在两个任务分支中采用统一的匹配块,从而降低了决策质量。此外,这些模型还难以解决决策错位的问题。本文提出了一种多注意力关联预测网络(MAPNet)来解决上述问题。具体来说,我们首先设计了两个新型匹配器,即类别感知匹配器和空间感知匹配器,通过有机整合自身、交叉、渠道或空间注意力来进行特征比较。它们分别能够充分捕捉用于分类的类别相关语义和用于回归的局部空间上下文。然后,我们提出了一个双对齐模块,以增强两个分支之间的对应关系,这有助于找到最佳跟踪方案。最后,我们介绍了基于所提预测网络的连体跟踪器,该跟踪器在 LaSOT、TrackingNet、GOT-10k、TNL2k 和 UAV123 等五个跟踪基准测试中取得了领先的性能,并超越了其他最先进的方法。
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Multi-attention associate prediction network for visual tracking
Classification-regression prediction networks have realized impressive success in several modern deep trackers. However, there is an inherent difference between classification and regression tasks, so they have diverse even opposite demands for feature matching. Existed models always ignore the key issue and only employ a unified matching block in two task branches, decaying the decision quality. Besides, these models also struggle with decision misalignment situation. In this paper, we propose a multi-attention associate prediction network (MAPNet) to tackle the above problems. Concretely, two novel matchers, i.e., category-aware matcher and spatial-aware matcher, are first designed for feature comparison by integrating self, cross, channel or spatial attentions organically. They are capable of fully capturing the category-related semantics for classification and the local spatial contexts for regression, respectively. Then, we present a dual alignment module to enhance the correspondences between two branches, which is useful to find the optimal tracking solution. Finally, we describe a Siamese tracker built upon the proposed prediction network, which achieves the leading performance on five tracking benchmarks, consisting of LaSOT, TrackingNet, GOT-10k, TNL2k and UAV123, and surpasses other state-of-the-art approaches.
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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