Deep Semantic Matching with Foreground Detection and Cycle-Consistency
This work addresses the problem of dense semantic correspondence for computer vision researchers, offering an incremental improvement over existing methods.
The paper tackles weakly supervised semantic matching between object instances by using foreground detection and cycle-consistency losses to handle background clutter and geometric inconsistencies, achieving favorable performance against state-of-the-art methods on datasets like PF-PASCAL, PF-WILLOW, and TSS.
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.