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

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11 引文 斯高帕斯(Scopus)


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
原文American English
主出版物標題Lecture Notes in Computer Science
發行者Springer Verlag
出版狀態Published - 2019


名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11363 LNCS


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