Search Results for author: Zheng Ge

Found 6 papers, 3 papers with code

YOLOX: Exceeding YOLO Series in 2021

1 code implementation18 Jul 2021 Zheng Ge, Songtao Liu, Feng Wang, Zeming Li, Jian Sun

In this report, we present some experienced improvements to YOLO series, forming a new high-performance detector -- YOLOX.

Autonomous Driving

OTA: Optimal Transport Assignment for Object Detection

1 code implementation CVPR 2021 Zheng Ge, Songtao Liu, Zeming Li, Osamu Yoshie, Jian Sun

Recent advances in label assignment in object detection mainly seek to independently define positive/negative training samples for each ground-truth (gt) object.

Object Detection

LLA: Loss-aware Label Assignment for Dense Pedestrian Detection

1 code implementation12 Jan 2021 Zheng Ge, JianFeng Wang, Xin Huang, Songtao Liu, Osamu Yoshie

A joint loss is then defined as the weighted summation of cls and reg losses as the assigning indicator.

Object Detection Pedestrian Detection

Delving into the Imbalance of Positive Proposals in Two-stage Object Detection

no code implementations23 May 2020 Zheng Ge, Zequn Jie, Xin Huang, Chengzheng Li, Osamu Yoshie

The first imbalance lies in the large number of low-quality RPN proposals, which makes the R-CNN module (i. e., post-classification layers) become highly biased towards the negative proposals in the early training stage.

Object Detection

NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing

no code implementations CVPR 2020 Xin Huang, Zheng Ge, Zequn Jie, Osamu Yoshie

To acquire the visible parts, a novel Paired-Box Model (PBM) is proposed to simultaneously predict the full and visible boxes of a pedestrian.

Pedestrian Detection

PS-RCNN: Detecting Secondary Human Instances in a Crowd via Primary Object Suppression

no code implementations16 Mar 2020 Zheng Ge, Zequn Jie, Xin Huang, Rong Xu, Osamu Yoshie

PS-RCNN first detects slightly/none occluded objects by an R-CNN module (referred as P-RCNN), and then suppress the detected instances by human-shaped masks so that the features of heavily occluded instances can stand out.

Human Detection Object Detection

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