Research map / Detection and segmentation / Tracking
Discriminative and Robust Online Learning for Siamese Visual Tracking
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add the online classifier and fuse its scores with SiamRPN++ scores | SiamRPN++ · OTB2015 | AUC: 0.696 -> 0.715 | Helped |
| add the online classifier and fuse its scores with SiamRPN++ scores | SiamRPN++ · VOT2018 | EAO: 0.414 -> 0.481 | Helped |
| add the online classifier and score fusion | SiamRPN++ · TrackingNet test | AUC: 0.733 -> 0.746 | Helped |
| set fusion weight lambda to 0.8 rather than lambda 0.0 (ablation) | DROL-RPN · LaSOT | AUC: 0.508 -> 0.532 | Helped |
| rely entirely on the online classifier by setting lambda to 1.0 (ablation) | DROL-RPN · LaSOT | AUC: 0.532 -> 0.391 | Hurt |
| apply compression before attention rather than attention before compression (ablation) | DROL-RPN · LaSOT | AUC: 0.528 -> 0.534 | Helped |
| update the short-term template every 5 frames rather than every frame (ablation) | DROL-RPN · LaSOT | AUC: 0.526 -> 0.537 | Helped |
| update the short-term template every 10 frames rather than every 5 frames (ablation) | DROL-RPN · LaSOT | AUC: 0.537 -> 0.533 | Hurt |
| guide template updates using the online score without fusing it with the siamese score (ablation) | DROL-RPN · LaSOT | AUC: 0.508 -> 0.513 | Helped |
Closest papers by meaning
- Deeper and Wider Siamese Networks for Real-Time Visual Tracking (CVPR 2019 · similarity 0.91)
- Deformable Siamese Attention Networks for Visual Object Tracking (CVPR 2020 · similarity 0.90)
- SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking (CVPR 2020 · similarity 0.90)
- SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines (AAAI 2020 · similarity 0.90)
- Learning To Fuse Asymmetric Feature Maps in Siamese Trackers (CVPR 2021 · similarity 0.90)
- SiamRPN++: Evolution of Siamese Visual Tracking With Very Deep Networks (CVPR 2019 · similarity 0.90)
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