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

Confluence is a novel non-Intersection over Union (IoU) alternative toNon-Maxima Suppression (NMS) in bounding box post-processing in objectdetection. It overcomes the inherent limitations of IoU-based NMS variants toprovide a more stable, consistent predictor of bounding box clustering by usinga normalized Manhattan Distance inspired proximity metric to represent boundingbox clustering. Unlike Greedy and Soft NMS, it does not rely solely onclassification confidence scores to select optimal bounding boxes, insteadselecting the box which is closest to every other box within a given clusterand removing highly confluent neighboring boxes. Confluence is experimentallyvalidated on the MS COCO and CrowdHuman benchmarks, improving Average Precisionby up to 2.3-3.8% and Average Recall by up to 5.3-7.2% when compared againstde-facto standard and state of the art NMS variants. Quantitative results aresupported by extensive qualitative analysis and threshold sensitivity analysisexperiments support the conclusion that Confluence is more robust than NMSvariants. Confluence represents a paradigm shift in bounding box processing,with potential to replace IoU in bounding box regression processes.

Confluence: A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection