  We present an approach for jointly matching and segmenting object instances
of the same category within a collection of images. In contrast to existing
algorithms that tackle the tasks of semantic matching and object
co-segmentation in isolation, our method exploits the complementary nature of
the two tasks. The key insights of our method are two-fold. First, the
estimated dense correspondence fields from semantic matching provide
supervision for object co-segmentation by enforcing consistency between the
predicted masks from a pair of images. Second, the predicted object masks from
object co-segmentation in turn allow us to reduce the adverse effects due to
background clutters for improving semantic matching. Our model is end-to-end
trainable and does not require supervision from manually annotated
correspondences and object masks. We validate the efficacy of our approach on
five benchmark datasets: TSS, Internet, PF-PASCAL, PF-WILLOW, and SPair-71k,
and show that our algorithm performs favorably against the state-of-the-art
methods on both semantic matching and object co-segmentation tasks.
