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

2 code implementations • 18 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

Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation

no code implementations • 5 Feb 2020 • Yingjie Cai, Buyu Li, Zeyu Jiao, Hongsheng Li, Xingyu Zeng, Xiaogang Wang

Monocular 3D object detection task aims to predict the 3D bounding boxes of objects based on monocular RGB images.

Depth Estimation Monocular 3D Object Detection +2

MMDetection: Open MMLab Detection Toolbox and Benchmark

144 code implementations • 17 Jun 2019 • Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, Dahua Lin

In this paper, we introduce the various features of this toolbox.

Benchmarking Instance Segmentation +2

Grid R-CNN Plus: Faster and Better

2 code implementations • 13 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

GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving

no code implementations • CVPR 2019 • Buyu Li, Wanli Ouyang, Lu Sheng, Xingyu Zeng, Xiaogang Wang

We present an efficient 3D object detection framework based on a single RGB image in the scenario of autonomous driving.

Autonomous Driving Monocular 3D Object Detection +3

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 implementations • 13 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