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

Drone navigation through natural language commands remains a significantchallenge due to the lack of publicly available multi-modal datasets and theintricate demands of fine-grained visual-text alignment. In response to thispressing need, we present a new human-computer interaction annotation benchmarkcalled GeoText-1652, meticulously curated through a robust Large Language Model(LLM)-based data generation framework and the expertise of pre-trained visionmodels. This new dataset seamlessly extends the existing image dataset, \ie,University-1652, with spatial-aware text annotations, encompassing intricateimage-text-bounding box associations. Besides, we introduce a new optimizationobjective to leverage fine-grained spatial associations, called blendingspatial matching, for region-level spatial relation matching. Extensiveexperiments reveal that our approach maintains an exceptional recall rate undervarying description complexities. This underscores the promising potential ofour approach in elevating drone control and navigation through the seamlessintegration of natural language commands in real-world scenarios.

Towards Natural Language-Guided Drones: GeoText-1652 Benchmark with Spatially Relation Matching