Proposal Flow: Semantic Correspondences from Object Proposals - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles IEEE Transactions on Pattern Analysis and Machine Intelligence Year : 2018

Proposal Flow: Semantic Correspondences from Object Proposals


Finding image correspondences remains a challenging problem in the presence of intra-class variations and large changes in scene layout. Semantic flow methods are designed to handle images depicting different instances of the same object or scene category. We introduce a novel approach to semantic flow, dubbed proposal flow, that establishes reliable correspondences using object proposals. Unlike prevailing semantic flow approaches that operate on pixels or regularly sampled local regions, proposal flow benefits from the characteristics of modern object proposals, that exhibit high repeatability at multiple scales, and can take advantage of both local and geometric consistency constraints among proposals. We also show that the corresponding sparse proposal flow can effectively be transformed into a conventional dense flow field. We introduce two new challenging datasets that can be used to evaluate both general semantic flow techniques and region-based approaches such as proposal flow. We use these benchmarks to compare different matching algorithms, object proposals, and region features within proposal flow, to the state of the art in semantic flow. This comparison, along with experiments on standard datasets, demonstrates that proposal flow significantly outperforms existing semantic flow methods in various settings.
Fichier principal
Vignette du fichier
proposal_flow_revised.pdf (7.2 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01644132 , version 1 (22-11-2017)



Bumsub Ham, Minsu Cho, Cordelia Schmid, Jean Ponce. Proposal Flow: Semantic Correspondences from Object Proposals. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40 (7), pp.1711-1725. ⟨10.1109/TPAMI.2017.2724510⟩. ⟨hal-01644132⟩
624 View
312 Download



Gmail Facebook Twitter LinkedIn More