Search Results for author: Mingyuan Mao

Found 6 papers, 3 papers with code

Oriented Object Detection with Transformer

no code implementations6 Jun 2021 Teli Ma, Mingyuan Mao, Honghui Zheng, Peng Gao, Xiaodi Wang, Shumin Han, Errui Ding, Baochang Zhang, David Doermann

Object detection with Transformers (DETR) has achieved a competitive performance over traditional detectors, such as Faster R-CNN.

Object Detection

Dual-stream Network for Visual Recognition

no code implementations NeurIPS 2021 Mingyuan Mao, Renrui Zhang, Honghui Zheng, Peng Gao, Teli Ma, Yan Peng, Errui Ding, Baochang Zhang, Shumin Han

Transformers with remarkable global representation capacities achieve competitive results for visual tasks, but fail to consider high-level local pattern information in input images.

Image Classification Instance Segmentation +2

PAFNet: An Efficient Anchor-Free Object Detector Guidance

1 code implementation28 Apr 2021 Ying Xin, Guanzhong Wang, Mingyuan Mao, Yuan Feng, Qingqing Dang, Yanjun Ma, Errui Ding, Shumin Han

Therefore, a trade-off between effectiveness and efficiency is necessary in practical scenarios.

 Ranked #1 on Object Detection on COCO test-dev (Hardware Burden metric)

Object Detection

The 1st Tiny Object Detection Challenge:Methods and Results

1 code implementation16 Sep 2020 Xuehui Yu, Zhenjun Han, Yuqi Gong, Nan Jiang, Jian Zhao, Qixiang Ye, Jie Chen, Yuan Feng, Bin Zhang, Xiaodi Wang, Ying Xin, Jingwei Liu, Mingyuan Mao, Sheng Xu, Baochang Zhang, Shumin Han, Cheng Gao, Wei Tang, Lizuo Jin, Mingbo Hong, Yuchao Yang, Shuiwang Li, Huan Luo, Qijun Zhao, Humphrey Shi

The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a current focus on tiny person detection.

Human Detection Object Detection

iffDetector: Inference-aware Feature Filtering for Object Detection

1 code implementation23 Jun 2020 Mingyuan Mao, Yuxin Tian, Baochang Zhang, Qixiang Ye, Wanquan Liu, Guodong Guo, David Doermann

In this paper, we propose a new feature optimization approach to enhance features and suppress background noise in both the training and inference stages.

Object Detection

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