4 research outputs found
Deep Semantic Matching with Foreground Detection and Cycle-Consistency
Establishing dense semantic correspondences between object instances remains
a challenging problem due to background clutter, significant scale and pose
differences, and large intra-class variations. In this paper, we address weakly
supervised semantic matching based on a deep network where only image pairs
without manual keypoint correspondence annotations are provided. To facilitate
network training with this weaker form of supervision, we 1) explicitly
estimate the foreground regions to suppress the effect of background clutter
and 2) develop cycle-consistent losses to enforce the predicted transformations
across multiple images to be geometrically plausible and consistent. We train
the proposed model using the PF-PASCAL dataset and evaluate the performance on
the PF-PASCAL, PF-WILLOW, and TSS datasets. Extensive experimental results show
that the proposed approach performs favorably against the state-of-the-art
methods.Comment: ACCV 2018. PAMI 2020 extension: arXiv:1906.0585