8 citations · 46 across the 11 of their papers we have counts for
11 papers
Miti-DETR: Object Detection based on Transformers with Mitigatory Self-Attention Convergence
Wenchi Ma, Tianxiao Zhang, Guanghui Wang
Object Detection with Transformers (DETR) and related works reach or even surpass the highly-optimized Faster-RCNN baseline with self-attention network architectures. Inspired by t…
Semantic Clustering based Deduction Learning for Image Recognition and Classification
Wenchi Ma, Xuemin Tu, Bo Luo +1
The paper proposes a semantic clustering based deduction learning by mimicking the learning and thinking process of human brains. Human beings can make judgments based on experienc…
Six-channel Image Representation for Cross-domain Object Detection
Tianxiao Zhang, Wenchi Ma, Guanghui Wang
Most deep learning models are data-driven and the excellent performance is highly dependent on the abundant and diverse datasets. However, it is very hard to obtain and label the d…
Why Layer-Wise Learning is Hard to Scale-up and a Possible Solution via Accelerated Downsampling
Wenchi Ma, Miao Yu, Kaidong Li +1
Layer-wise learning, as an alternative to global back-propagation, is easy to interpret, analyze, and it is memory efficient. Recent studies demonstrate that layer-wise learning ca…
Multi-Resolution Fusion and Multi-scale Input Priors Based Crowd Counting
Usman Sajid, Wenchi Ma, Guanghui Wang
Crowd counting in still images is a challenging problem in practice due to huge crowd-density variations, large perspective changes, severe occlusion, and variable lighting conditi…
Location-Aware Box Reasoning for Anchor-Based Single-Shot Object Detection
Wenchi Ma, Kaidong Li, Guanghui Wang
In the majority of object detection frameworks, the confidence of instance classification is used as the quality criterion of predicted bounding boxes, like the confidence-based ra…
Self-Orthogonality Module: A Network Architecture Plug-in for Learning Orthogonal Filters
Ziming Zhang, Wenchi Ma, Yuanwei Wu +1
In this paper, we investigate the empirical impact of orthogonality regularization (OR) in deep learning, either solo or collaboratively. Recent works on OR showed some promising r…
MDFN: Multi-Scale Deep Feature Learning Network for Object Detection
Wenchi Ma, Yuanwei Wu, Feng Cen +1
This paper proposes an innovative object detector by leveraging deep features learned in high-level layers. Compared with features produced in earlier layers, the deep features are…
Object Detection with Convolutional Neural Networks
Kaidong Li, Wenchi Ma, Usman Sajid +2
In this chapter, we present a brief overview of the recent development in object detection using convolutional neural networks (CNN). Several classical CNN-based detectors are pres…
Adaptively Denoising Proposal Collection for Weakly Supervised Object Localization
Wenju Xu, Yuanwei Wu, Wenchi Ma +1
In this paper, we address the problem of weakly supervised object localization (WSL), which trains a detection network on the dataset with only image-level annotations. The propose…
MDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for Efficient Object Detection
Wenchi Ma, Yuanwei Wu, Zongbo Wang +1
Object detection in challenging situations such as scale variation, occlusion, and truncation depends not only on feature details but also on contextual information. Most previous…