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

The advent of deep learning has brought an impressive advance to monoculardepth estimation, e.g., supervised monocular depth estimation has beenthoroughly investigated. However, the large amount of the RGB-to-depth datasetmay not be always available since collecting accurate depth ground truthaccording to the RGB image is a time-consuming and expensive task. Although thenetwork can be trained on an alternative dataset to overcome the dataset scaleproblem, the trained model is hard to generalize to the target domain due tothe domain discrepancy. Adversarial domain alignment has demonstrated itsefficacy to mitigate the domain shift on simple image classification tasks inprevious works. However, traditional approaches hardly handle the conditionalalignment as they solely consider the feature map of the network. In thispaper, we propose an adversarial training model that leverages semanticinformation to narrow the domain gap. Based on the experiments conducted on thedatasets for the monocular depth estimation task including KITTI andCityscapes, the proposed compact model achieves state-of-the-art performancecomparable to complex latest models and shows favorable results on boundariesand objects at far distances.

Domain Adaptive Monocular Depth Estimation With Semantic Information