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/

Global: Zendy Plus annual

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

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Leveraging synthetically rendered data offers great potential to improvemonocular depth estimation and other geometric estimation tasks, but closingthe synthetic-real domain gap is a non-trivial and important task. While muchrecent work has focused on unsupervised domain adaptation, we consider a morerealistic scenario where a large amount of synthetic training data issupplemented by a small set of real images with ground-truth. In this setting,we find that existing domain translation approaches are difficult to train andoffer little advantage over simple baselines that use a mix of real andsynthetic data. A key failure mode is that real-world images contain novelobjects and clutter not present in synthetic training. This high-level domainshift isn't handled by existing image translation models.  Based on these observations, we develop an attention module that learns toidentify and remove difficult out-of-domain regions in real images in order toimprove depth prediction for a model trained primarily on synthetic data. Wecarry out extensive experiments to validate our attend-remove-complete approach(ARC) and find that it significantly outperforms state-of-the-art domainadaptation methods for depth prediction. Visualizing the removed regionsprovides interpretable insights into the synthetic-real domain gap.

Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation