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

Learning to compose visual relationships from raw images in the form of scenegraphs is a highly challenging task due to contextual dependencies, but it isessential in computer vision applications that depend on scene understanding.However, no current approaches in Scene Graph Generation (SGG) aim at providinguseful graphs for downstream tasks. Instead, the main focus has primarily beenon the task of unbiasing the data distribution for predicting more fine-grainedrelations. That being said, all fine-grained relations are not equally relevantand at least a part of them are of no use for real-world applications. In thiswork, we introduce the task of Efficient SGG that prioritizes the generation ofrelevant relations, facilitating the use of Scene Graphs in downstream taskssuch as Image Generation. To support further approaches, we present a newdataset, VG150-curated, based on the annotations of the popular Visual Genomedataset. We show through a set of experiments that this dataset contains morehigh-quality and diverse annotations than the one usually use in SGG. Finally,we show the efficiency of this dataset in the task of Image Generation fromScene Graphs.

Fine-Grained is Too Coarse: A Novel Data-Centric Approach for Efficient Scene Graph Generation