Research map / Label-efficient and robust learning / Domain adaptation and generalization
Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use multi-domain pretraining instead of single-domain learning on data from multiple sequences (ablation) | MDNet · OTB100 | tracking performance | Helped |
| Add bounding box regression (ablation) | MDNet · OTB100 | tracking performance | Helped |
| Add hard negative mining (ablation) | MDNet without bounding box regression · OTB100 | tracking performance | Helped |
Closest papers by meaning
- STCT: Sequentially Training Convolutional Networks for Visual Tracking (CVPR 2016 · similarity 0.91)
- Release the Power of Online-Training for Robust Visual Tracking (AAAI 2020 · similarity 0.90)
- Target-Aware Deep Tracking (CVPR 2019 · similarity 0.89)
- Online Multi-Target Tracking Using Recurrent Neural Networks (AAAI 2017 · similarity 0.88)
- Video Diffusion Models Excel at Tracking Similar-Looking Objects Without Supervision (NeurIPS 2025 · similarity 0.86)
- VastTrack: Vast Category Visual Object Tracking (NeurIPS 2024 · similarity 0.86)
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