195 citations · 301 across the 29 of their papers we have counts for
9 papers · 1 filter
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…
Stacked Wasserstein Autoencoder
Wenju Xu, Shawn Keshmiri, Guanghui Wang
Approximating distributions over complicated manifolds, such as natural images, are conceptually attractive. The deep latent variable model, trained using variational autoencoders…
Direct Visual-Inertial Odometry with Semi-Dense Mapping
Wenju Xu, Dongkyu Choi, Guanghui Wang
The paper presents a direct visual-inertial odometry system. In particular, a tightly coupled nonlinear optimization based method is proposed by integrating the recent advances in…
Towards Learning Affine-Invariant Representations via Data-Efficient CNNs
Xenju Xu, Guanghui Wang, Alan Sullivan +1
In this paper we propose integrating a priori knowledge into both design and training of convolutional neural networks (CNNs) to learn object representations that are invariant to…