Summary of Improving Accuracy and Generalization For Efficient Visual Tracking, by Ram Zaveri and Shivang Patel and Yu Gu and Gianfranco Doretto
Improving Accuracy and Generalization for Efficient Visual Tracking
by Ram Zaveri, Shivang Patel, Yu Gu, Gianfranco Doretto
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces SiamABC, a highly efficient Siamese tracker that improves tracking performance, even on out-of-distribution (OOD) sequences. It achieves this by bridging the dynamic variability of the target through new architectural designs and new losses for training. The model also includes a fast backward-free dynamic test-time adaptation method to continuously adapt to visual changes. The experiments show remarkable performance gains in OOD sets while maintaining accurate performance on in-distribution benchmarks, outperforming MixFormerV2-S by 7.6% on the AVisT benchmark and being 3x faster (100 FPS) on a CPU. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces SiamABC, a new visual tracker that can work well even when it sees things it hasn’t seen before. This is important because current trackers tend to do really well when they’re shown things they’ve seen before, but not so well when they see something new. SiamABC is designed to be fast and efficient, and it does this by using a special way of training the model and adapting to changes in what it’s seeing. The results show that SiamABC can track things really well even when it hasn’t seen them before, which is important for using trackers in real-world situations. |
Keywords
» Artificial intelligence » Tracking
