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

We propose to compose dynamic tree structures that place the objects in animage into a visual context, helping visual reasoning tasks such as scene graphgeneration and visual Q&A. Our visual context tree model, dubbed VCTree, hastwo key advantages over existing structured object representations includingchains and fully-connected graphs: 1) The efficient and expressive binary treeencodes the inherent parallel/hierarchical relationships among objects, e.g.,"clothes" and "pants" are usually co-occur and belong to "person"; 2) thedynamic structure varies from image to image and task to task, allowing morecontent-/task-specific message passing among objects. To construct a VCTree, wedesign a score function that calculates the task-dependent validity betweeneach object pair, and the tree is the binary version of the maximum spanningtree from the score matrix. Then, visual contexts are encoded by bidirectionalTreeLSTM and decoded by task-specific models. We develop a hybrid learningprocedure which integrates end-task supervised learning and the tree structurereinforcement learning, where the former's evaluation result serves as aself-critic for the latter's structure exploration. Experimental results on twobenchmarks, which require reasoning over contexts: Visual Genome for scenegraph generation and VQA2.0 for visual Q&A, show that VCTree outperformsstate-of-the-art results while discovering interpretable visual contextstructures.

Learning to Compose Dynamic Tree Structures for Visual Contexts