78 citations · 217 across the 23 of their papers we have counts for
16 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…
Deformable Gabor Feature Networks for Biomedical Image Classification
Xuan Gong, Xin Xia, Wentao Zhu +3
In recent years, deep learning has dominated progress in the field of medical image analysis. We find however, that the ability of current deep learning approaches to represent the…
Binarized Neural Architecture Search for Efficient Object Recognition
Hanlin Chen, Li'an Zhuo, Baochang Zhang +5
Traditional neural architecture search (NAS) has a significant impact in computer vision by automatically designing network architectures for various tasks. In this paper, binarize…
Anti-Bandit Neural Architecture Search for Model Defense
Hanlin Chen, Baochang Zhang, Song Xue +4
Deep convolutional neural networks (DCNNs) have dominated as the best performers in machine learning, but can be challenged by adversarial attacks. In this paper, we defend against…
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…