Learning Tracking Representations from Single Point Annotations
Qiangqiang Wu Antoni B. Chan
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 learning. Our SoCL consists of adaptive positive and
negative sample generation, which is memory-efficient and effective for
learning tracking representations. We apply the learned representation of SoCL
to visual tracking and show that our method can 1) achieve better performance
than the fully supervised baseline trained with box annotations under the same
annotation time cost; 2) achieve comparable performance of the fully supervised
baseline by using the same number of training frames and meanwhile reducing
annotation time cost by 78% and total fees by 85%; 3) be robust to annotation
noise.