Deep Semantic Matching with Foreground Detection and Cycle-Consistency

Yun Chun Chen, Po Hsiang Huang, Li Yu Yu, Jia Bin Huang, Ming Hsuan Yang, Yen Yu Lin

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

11 Scopus citations


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. The code and model will be available at
Original languageAmerican English
Title of host publicationLecture Notes in Computer Science
PublisherSpringer Verlag
Number of pages16
ISBN (Print)9783030208929
StatePublished - 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11363 LNCS


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