Scholarlink

<p>In order to balance the tracking performance and inference speed, a lightweight Siamese-based tracker named SiamAHG is proposed in this paper. It employs the lightweight network ShuffleNet V2 for feature extraction and a novel adaptive hierarchical graph attention for feature fusion. Specifically, the feature fusion sub-network consists of an adaptive feature map refinement module (AFMRM) and a hierarchical graph attention module (HGAM). AFMRM refines feature maps of different stages and strengthens the model expression by combining multiple convolutional kernels dynamically based upon diverse attentions. HGAM realizes node-level information interaction of the same stage between the template and search branches, and integrates the features of different stages to get complementary information for tracking. Experiments on LaSOT, OTB100, GOT-10k, and UAV123 validate the effectiveness and efficiency of our proposed tracker. It has significant advantages in model complexity and inference speed, while achieving competitive results with state-of-the-art trackers.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

SiamAHG: adaptive hierarchical graph attention for lightweight siamese tracking

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月28日

查看全文
  • Na Li,
  • Yaofu Fan,
  • Xuhao Chen,
  • Xinyu Liu,
  • Jinglu He

摘要

In order to balance the tracking performance and inference speed, a lightweight Siamese-based tracker named SiamAHG is proposed in this paper. It employs the lightweight network ShuffleNet V2 for feature extraction and a novel adaptive hierarchical graph attention for feature fusion. Specifically, the feature fusion sub-network consists of an adaptive feature map refinement module (AFMRM) and a hierarchical graph attention module (HGAM). AFMRM refines feature maps of different stages and strengthens the model expression by combining multiple convolutional kernels dynamically based upon diverse attentions. HGAM realizes node-level information interaction of the same stage between the template and search branches, and integrates the features of different stages to get complementary information for tracking. Experiments on LaSOT, OTB100, GOT-10k, and UAV123 validate the effectiveness and efficiency of our proposed tracker. It has significant advantages in model complexity and inference speed, while achieving competitive results with state-of-the-art trackers.