  Efficient single instance segmentation is essential for unlocking features in
the mobile imaging applications, such as capture or editing. Existing
on-the-fly mobile imaging applications scope the segmentation task to portraits
or the salient subject due to the computational constraints. Instance
segmentation, despite its recent developments towards efficient networks, is
still heavy due to the cost of computation on the entire image to identify all
instances. To address this, we propose and formulate a one tap driven single
instance segmentation task that segments a single instance selected by a user
via a positive tap. This task, in contrast to the broader task of segmenting
anything as suggested in the Segment Anything Model \cite{sam}, focuses on
efficient segmentation of a single instance specified by the user. To solve
this problem, we present TraceNet, which explicitly locates the selected
instance by way of receptive field tracing. TraceNet identifies image regions
that are related to the user tap and heavy computations are only performed on
selected regions of the image. Therefore overall computation cost and memory
consumption are reduced during inference. We evaluate the performance of
TraceNet on instance IoU average over taps and the proportion of the region
that a user tap can fall into for a high-quality single-instance mask.
Experimental results on MS-COCO and LVIS demonstrate the effectiveness and
efficiency of the proposed approach. TraceNet can jointly achieve the
efficiency and interactivity, filling in the gap between needs for efficient
mobile inference and recent research trend towards multimodal and interactive
segmentation models.
