Figure 3: SeSAM framework for coarse sup ervision. First, pseudo-labels for training images are
generated by the teacher model. Next, these pseudo-labels are then refined following the key steps 1-3,
namely instance separation, point sampling and mask selection (see Section 3). To improve mask quality,
we augment coarse labels with high-confidence pseudo-labels before applying SAM (step 0). The resulting
refined labels, called SeSAM labels, are then used to train the student network.