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/

Zendy Plus

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

Zendy Tools

Zendy Open

Greedy-NMS inherently raises a dilemma, where a lower NMS threshold willpotentially lead to a lower recall rate and a higher threshold introduces morefalse positives. This problem is more severe in pedestrian detection becausethe instance density varies more intensively. However, previous works on NMSdon't consider or vaguely consider the factor of the existent of nearbypedestrians. Thus, we propose Nearby Objects Hallucinator (NOH), whichpinpoints the objects nearby each proposal with a Gaussian distribution,together with NOH-NMS, which dynamically eases the suppression for the spacethat might contain other objects with a high likelihood. Compared toGreedy-NMS, our method, as the state-of-the-art, improves by $3.9\%$ AP,$5.1\%$ Recall, and $0.8\%$ $\text{MR}^{-2}$ on CrowdHuman to $89.0\%$ AP and$92.9\%$ Recall, and $43.9\%$ $\text{MR}^{-2}$ respectively.

NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination