Search Results for author: Buyu Li

Found 9 papers, 6 papers with code

DanceFormer: Music Conditioned 3D Dance Generation with Parametric Motion Transformer

2 code implementations18 Mar 2021 Buyu Li, Yongchi Zhao, Zhelun Shi, Lu Sheng

In this paper, we reformulate it by a two-stage process, ie, a key pose generation and then an in-between parametric motion curve prediction, where the key poses are easier to be synchronized with the music beats and the parametric curves can be efficiently regressed to render fluent rhythm-aligned movements.

MimicDet: Bridging the Gap Between One-Stage and Two-Stage Object Detection

no code implementations ECCV 2020 Xin Lu, Quanquan Li, Buyu Li, Junjie Yan

In this paper, we propose MimicDet, a novel and efficient framework to train a one-stage detector by directly mimic the two-stage features, aiming to bridge the accuracy gap between one-stage and two-stage detectors.

object-detection Object Detection

Equalization Loss for Long-Tailed Object Recognition

1 code implementation CVPR 2020 Jingru Tan, Changbao Wang, Buyu Li, Quanquan Li, Wanli Ouyang, Changqing Yin, Junjie Yan

Based on it, we propose a simple but effective loss, named equalization loss, to tackle the problem of long-tailed rare categories by simply ignoring those gradients for rare categories.

Long-tail Learning Object +3

Grid R-CNN Plus: Faster and Better

2 code implementations13 Jun 2019 Xin Lu, Buyu Li, Yuxin Yue, Quanquan Li, Junjie Yan

Grid R-CNN is a well-performed objection detection framework.

Object Detection regression

Grid R-CNN

2 code implementations CVPR 2019 Xin Lu, Buyu Li, Yuxin Yue, Quanquan Li, Junjie Yan

This paper proposes a novel object detection framework named Grid R-CNN, which adopts a grid guided localization mechanism for accurate object detection.

Novel Object Detection Object +3

Gradient Harmonized Single-stage Detector

9 code implementations13 Nov 2018 Buyu Li, Yu Liu, Xiaogang Wang

Despite the great success of two-stage detectors, single-stage detector is still a more elegant and efficient way, yet suffers from the two well-known disharmonies during training, i. e. the huge difference in quantity between positive and negative examples as well as between easy and hard examples.

General Classification Object Detection +1

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