Research map / Detection and segmentation / Tracking
Graph Attention Tracking
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use target-aware template area selection with GAM instead of fixed-region cropping (ablation) | SiamGAT · UAV123 | Success: +2.0% | Helped |
| use target-aware template area selection with GAM instead of fixed-region cropping (ablation) | SiamGAT · UAV123 | Precision: +2.1% | Helped |
| replace target-aware GAM with DW-Xcorr (ablation) | SiamGAT · UAV123 | Success: 3.1% | Hurt |
| replace target-aware GAM with DW-Xcorr (ablation) | SiamGAT · UAV123 | Precision: 2.8% | Hurt |
| replace AlexNet with GoogLeNet (ablation) | SiamGAT · UAV123 | Success: from 59.2% to 64.6%; +5.4% | Helped |
| replace AlexNet with GoogLeNet (ablation) | SiamGAT · UAV123 | FPS: from 165 FPS to 70 FPS | Hurt |
| use SiamGAT instead of SiamCAR | SiamGAT · UAV123 | Precision: +3.0% | Helped |
| use SiamGAT instead of SiamCAR | SiamGAT · GOT-10k | AO: +4.8% | Helped |
| use SiamGAT instead of Ocean-online | SiamGAT · GOT-10k | AO: +1.6% | Helped |
| use SiamGAT instead of Ocean-online | SiamGAT · LaSOT | Normalized precision | Hurt |
| use SiamGAT instead of SiamCAR | SiamGAT · OTB-100, fast motion, out-of-view, and low resolution attributes | Success | Hurt |
| use SiamGAT instead of SiamCAR | SiamGAT · OTB-100, deformation, rotation, occlusion, illumination variation, and scale variation attributes | Success | Helped |
Closest papers by meaning
- Deformable Siamese Attention Networks for Visual Object Tracking (CVPR 2020 · similarity 0.92)
- Graph Convolutional Tracking (CVPR 2019 · similarity 0.91)
- A Twofold Siamese Network for Real-Time Object Tracking (CVPR 2018 · similarity 0.90)
- Correlation-Aware Deep Tracking (CVPR 2022 · similarity 0.90)
- Ranking-Based Siamese Visual Tracking (CVPR 2022 · similarity 0.90)
- High Performance Visual Tracking With Siamese Region Proposal Network (CVPR 2018 · similarity 0.90)
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