AIRHF-Net: an adaptive interaction representation hierarchical fusion network for occluded person re-identification
Abstract To tackle the high resource consumption in occluded person re-identification, sparse attention mechanisms based on Vision Transformers (ViTs) have become popular. However, they often suffer from performance degradation with long sequences, omission of crucial information, and token represen...
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Main Authors: | Shuze Geng, Qiudong Yu, Haowei Wang, Ziyi Song |
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Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2024-11-01
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Series: | Scientific Reports |
Subjects: | |
Online Access: | https://doi.org/10.1038/s41598-024-76781-4 |
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