用轻量微调技术提升目标跟踪精度，竞赛夺冠
The Solution for Single Object Tracking Task of Perception Test Challenge 2024
- 采用LoRA仅微调少量参数，保持推理速度
- 在LaSOT与GOT-10k数据集上训练，获0.813分
- 适合追求高效高精度跟踪的工程应用
本报告介绍我们用于单目标跟踪（SOT）的方法，旨在视频序列中持续追踪指定目标。我们采用LoRAT方法，核心是将LoRA技术——一种无需增加推理延迟即可微调模型少量参数的方法——应用于视觉跟踪领域。模型在大规模LaSOT和GOT-10k数据集上进行训练，为性能提供坚实基础。此外，我们引入alpha-refine技术对边界框输出进行后处理，尽管该方法未达预期效果，但整体方法仍取得0.813的得分，在竞赛中位列第一。
原文摘要 · Abstract (English)
This report presents our method for Single Object Tracking (SOT), which aims to track a specified object throughout a video sequence. We employ the LoRAT method. The essence of the work lies in adapting LoRA, a technique that fine-tunes a small subset of model parameters without adding inference latency, to the domain of visual tracking. We train our model using the extensive LaSOT and GOT-10k datasets, which provide a solid foundation for robust performance. Additionally, we implement the alpha-refine technique for post-processing the bounding box outputs. Although the alpha-refine method does not yield the anticipated results, our overall approach achieves a score of 0.813, securing first place in the competition.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。