32 citations · 43 across the 3 of their papers we have counts for
6 papers · 1 filter
Oriented Object Detection with Transformer
Teli Ma, Mingyuan Mao, Honghui Zheng +6
Object detection with Transformers (DETR) has achieved a competitive performance over traditional detectors, such as Faster R-CNN. However, the potential of DETR remains largely un…
Probabilistic Ranking-Aware Ensembles for Enhanced Object Detections
Mingyuan Mao, Baochang Zhang, David Doermann +5
Model ensembles are becoming one of the most effective approaches for improving object detection performance already optimized for a single detector. Conventional methods directly…
Dual-stream Network for Visual Recognition
Mingyuan Mao, Renrui Zhang, Honghui Zheng +6
Transformers with remarkable global representation capacities achieve competitive results for visual tasks, but fail to consider high-level local pattern information in input image…
PAFNet: An Efficient Anchor-Free Object Detector Guidance
Ying Xin, Guanzhong Wang, Mingyuan Mao +5
Object detection is a basic but challenging task in computer vision, which plays a key role in a variety of industrial applications. However, object detectors based on deep learnin…
The 1st Tiny Object Detection Challenge:Methods and Results
Xuehui Yu, Zhenjun Han, Yuqi Gong +22
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…
iffDetector: Inference-aware Feature Filtering for Object Detection
Mingyuan Mao, Yuxin Tian, Baochang Zhang +4
Modern CNN-based object detectors focus on feature configuration during training but often ignore feature optimization during inference. In this paper, we propose a new feature opt…