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- W3044227383 abstract "Greedy-NMS inherently raises a dilemma, where a lower NMS threshold will potentially lead to a lower recall rate and a higher threshold introduces more false positives. This problem is more severe in pedestrian detection because the instance density varies more intensively. However, previous works on NMS don't consider or vaguely consider the factor of the existent of nearby pedestrians. Thus, we propose Nearby Objects Hallucinator (NOH), which pinpoints the objects nearby each proposal with a Gaussian distribution, together with NOH-NMS, which dynamically eases the suppression for the space that might contain other objects with a high likelihood. Compared to Greedy-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." @default.
- W3044227383 created "2020-07-29" @default.
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- W3044227383 date "2020-07-27" @default.
- W3044227383 modified "2023-09-23" @default.
- W3044227383 title "NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination" @default.
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