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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-05-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44443-025-00061-y |
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