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Learning Tracking Representations from Single Point Annotations
April 16, 2024, 4:47 a.m. | Qiangqiang Wu, Antoni B. Chan
cs.CV updates on arXiv.org arxiv.org
Abstract: Existing deep trackers are typically trained with largescale video frames with annotated bounding boxes. However, these bounding boxes are expensive and time-consuming to annotate, in particular for large scale datasets. In this paper, we propose to learn tracking representations from single point annotations (i.e., 4.5x faster to annotate than the traditional bounding box) in a weakly supervised manner. Specifically, we propose a soft contrastive learning (SoCL) framework that incorporates target objectness prior into end-to-end contrastive …
abstract annotations arxiv cs.cv datasets faster however learn paper scale tracking type video
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