SiamAHG: adaptive hierarchical graph attention for lightweight siamese tracking
Abstract 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. Specif...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-05-01
|
| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44443-025-00061-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract 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. |
|---|---|
| ISSN: | 1319-1578 2213-1248 |