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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

For most of the object detectors based on multi-scale feature maps, theshallow layers are rich in fine spatial information and thus mainly responsiblefor small object detection. The performance of small object detection, however,is still less than satisfactory because of the deficiency of semanticinformation on shallow feature maps. In this paper, we design a Multi-scaleDeconvolutional Single Shot Detector (MDSSD), especially for small objectdetection. In MDSSD, multiple high-level feature maps at different scales areupsampled simultaneously to increase the spatial resolution. Afterwards, weimplement the skip connections with low-level feature maps via Fusion Block.The fusion feature maps, named Fusion Module, are of strong featurerepresentational power of small instances. It is noteworthy that thesehigh-level feature maps utilized in Fusion Block preserve both strong semanticinformation and some fine details of small instances, rather than the top-mostlayer where the representation of fine details for small objects arepotentially wiped out. The proposed framework achieves 77.6% mAP for smallobject detection on the challenging dataset TT100K with 512 x 512 input,outperforming other detectors with a large margin. Moreover, it can alsoachieve state-of-the-art results for general object detection on PASCAL VOC2007test and MS COCO test-dev2015, especially achieving 2 to 5 points improvementon small object categories.

MDSSD: Multi-scale Deconvolutional Single Shot Detector for Small Objects