  Collecting image annotations remains a significant burden when deploying CNN
in a specific applicative context. This is especially the case when the
annotation consists in binary masks covering object instances. Our work
proposes to delineate instances in three steps, based on a semi-automatic
approach: (1) the extreme points of an object (left-most, right-most, top,
bottom pixels) are manually defined, thereby providing the object bounding-box,
(2) a universal automatic segmentation tool like Deep Extreme Cut is used to
turn the bounded object into a segmentation mask that matches the extreme
points; and (3) the predicted mask is manually corrected. Various strategies
are then investigated to balance the human manual annotation resources between
bounding-box definition and mask correction, including when the correction of
instance masks is prioritized based on their overlap with other instance
bounding-boxes, or the outcome of an instance segmentation model trained on a
partially annotated dataset. Our experimental study considers a teamsport
player segmentation task, and measures how the accuracy of the Panoptic-Deeplab
instance segmentation model depends on the human annotation resources
allocation strategy. It reveals that the sole definition of extreme points
results in a model accuracy that would require up to 10 times more resources if
the masks were defined through fully manual delineation of instances. When
targeting higher accuracies, prioritizing the mask correction among the
training set instances is also shown to save up to 80\% of correction
annotation resources compared to a systematic frame by frame correction of
instances, for a same trained instance segmentation model accuracy.
