405 citations · 2.3k across the 116 of their papers we have counts for
12 papers · 2 filters
A Deep Learning Approach to Drone Monitoring
Yueru Chen, Pranav Aggarwal, Jongmoo Choi +1
A drone monitoring system that integrates deep-learning-based detection and tracking modules is proposed in this work. The biggest challenge in adopting deep learning methods for d…
Multiple Instance Curriculum Learning for Weakly Supervised Object Detection
Siyang Li, Xiangxin Zhu, Qin Huang +2
When supervising an object detector with weakly labeled data, most existing approaches are prone to trapping in the discriminative object parts, e.g., finding the face of a cat ins…
A Taught-Obesrve-Ask (TOA) Method for Object Detection with Critical Supervision
Chi-Hao Wu, Qin Huang, Siyang Li +1
Being inspired by child's learning experience - taught first and followed by observation and questioning, we investigate a critically supervised learning methodology for object det…
Contextual-based Image Inpainting: Infer, Match, and Translate
Yuhang Song, Chao Yang, Zhe Lin +4
We study the task of image inpainting, which is to fill in the missing region of an incomplete image with plausible contents. To this end, we propose a learning-based approach to g…
A Fully Convolutional Tri-branch Network (FCTN) for Domain Adaptation
Junting Zhang, Chen Liang, C. -C. Jay Kuo
A domain adaptation method for urban scene segmentation is proposed in this work. We develop a fully convolutional tri-branch network, where two branches assign pseudo labels to im…
A Saak Transform Approach to Efficient, Scalable and Robust Handwritten Digits Recognition
Yueru Chen, Zhuwei Xu, Shanshan Cai +2
An efficient, scalable and robust approach to the handwritten digits recognition problem based on the Saak transform is proposed in this work. First, multi-stage Saak transforms ar…
On Data-Driven Saak Transform
C. -C. Jay Kuo, Yueru Chen
Being motivated by the multilayer RECOS (REctified-COrrelations on a Sphere) transform, we develop a data-driven Saak (Subspace approximation with augmented kernels) transform in t…
Image Splicing Localization Using A Multi-Task Fully Convolutional Network (MFCN)
Ronald Salloum, Yuzhuo Ren, C. -C. Jay Kuo
In this work, we propose a technique that utilizes a fully convolutional network (FCN) to localize image splicing attacks. We first evaluated a single-task FCN (SFCN) trained only…
Semantic Segmentation with Reverse Attention
Qin Huang, Chunyang Xia, Chihao Wu +4
Recent development in fully convolutional neural network enables efficient end-to-end learning of semantic segmentation. Traditionally, the convolutional classifiers are taught to…
CNN as Guided Multi-layer RECOS Transform
C. -C. Jay Kuo
There is a resurging interest in developing a neural-network-based solution to the supervised machine learning problem. The convolutional neural network (CNN) will be studied in th…
Design, Analysis and Application of A Volumetric Convolutional Neural Network
Xiaqing Pan, Yueru Chen, C. -C. Jay Kuo
The design, analysis and application of a volumetric convolutional neural network (VCNN) are studied in this work. Although many CNNs have been proposed in the literature, their de…
3D Shape Retrieval via Irrelevance Filtering and Similarity Ranking (IF/SR)
Xiaqing Pan, Yueru Chen, C. -C. Jay Kuo
A novel solution for the content-based 3D shape retrieval problem using an unsupervised clustering approach, which does not need any label information of 3D shapes, is presented in…