Research map / Detection and segmentation / Tracking
ATOM: Accurate Tracking by Overlap Maximization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add target-specific feature modulation using the reference frame (ablation) | ATOM IoU prediction · Combined UAV123 and NFS | AUC (%): 56.7 -> 62.3 | Helped |
| use feature modulation instead of concatenating reference and test features (ablation) | ATOM IoU prediction · Combined UAV123 and NFS | AUC (%): 56.3 -> 62.3 | Helped |
| use feature modulation instead of a Siamese architecture (ablation) | ATOM IoU prediction · Combined UAV123 and NFS | AUC (%): 61.7 -> 62.3 | Helped |
| combine Block 3 and Block 4 features (ablation) | ATOM IoU prediction · Combined UAV123 and NFS | AUC (%): 60.3 -> 62.3; 58.5 -> 62.3 | Helped |
| use overlap-maximization state estimation instead of brute-force multi-scale search (ablation) | ATOM · Combined UAV123 and NFS | AUC (%): 53.7 -> 62.3 | Helped |
| add the online target classification component (ablation) | ATOM · Combined UAV123 and NFS | AUC (%): 43.0 -> 62.3 | Helped |
| use Gauss-Newton and Conjugate Gradient instead of gradient descent (ablation) | ATOM online classifier · Combined UAV123 and NFS | AUC (%): 60.9 -> 62.3 | Helped |
| increase gradient-descent iterations fivefold (ablation) | ATOM online classifier · Combined UAV123 and NFS | AUC (%): 60.9 -> 61.1 | No clear effect |
| add hard negative mining (ablation) | ATOM online classifier · Combined UAV123 and NFS | AUC (%): 61.9 -> 62.3; 0.4% AUC | Helped |
| train the IoU predictor with recent tracking datasets and COCO instead of ImageNet-VID only (ablation) | ATOM · LaSOT | AUC (%): 2.0% | Helped |
| train the IoU predictor with recent tracking datasets and COCO instead of ImageNet-VID only (ablation) | ATOM · TrackingNet | AUC (%): 0.5% | Helped |
Closest papers by meaning
- ADA-Track: End-to-End Multi-Camera 3D Multi-Object Tracking with Alternating Detection and Association (CVPR 2024 · similarity 0.86)
- S2-Track: A Simple yet Strong Approach for End-to-End 3D Multi-Object Tracking (ICML 2025 · similarity 0.86)
- DiffusionTrack: Diffusion Model for Multi-Object Tracking (AAAI 2024 · similarity 0.86)
- A Distractor-Aware Memory for Visual Object Tracking with SAM2 (CVPR 2025 · similarity 0.86)
- Track-On: Transformer-based Online Point Tracking with Memory (ICLR 2025 · similarity 0.85)
- Focusing on Tracks for Online Multi-Object Tracking (CVPR 2025 · similarity 0.85)
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