English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

In this paper we attempt to address the problem of geometric multi-modelfitting with resorting to a few weakly annotated (WA) data points, which hasbeen sparsely studied so far. In weak annotating, most of the manualannotations are supposed to be correct yet inevitably mixed with incorrectones. The WA data can be naturally obtained in an interactive way for specifictasks, for example, in the case of homography estimation, one can easilyannotate points on the same plane/object with a single label by observing theimage. Motivated by this, we propose a novel method to make full use of the WAdata to boost the multi-model fitting performance. Specifically, a graph formodel proposal sampling is first constructed using the WA data, given the priorthat the WA data annotated with the same weak label has a high probability ofbeing assigned to the same model. By incorporating this prior knowledge intothe calculation of edge probabilities, vertices (i.e., data points) lie on/nearthe latent model are likely to connect together and further form asubset/cluster for effective proposals generation. With the proposalsgenerated, the $\alpha$-expansion is adopted for labeling, and our method inreturn updates the proposals. This works in an iterative way. Extensiveexperiments validate our method and show that the proposed method producesnoticeably better results than state-of-the-art techniques in most cases.

G2MF-WA: Geometric Multi-Model Fitting with Weakly Annotated Data