activity
20152021
most citedDepth Adaptive Deep Neural Network for Semantic Segmentation

74 citations · 132 across the 10 of their papers we have counts for

collaborators

21 papers

cs.CV2021

SM3D: Simultaneous Monocular Mapping and 3D Detection

Runfa Li, Truong Nguyen

Mapping and 3D detection are two major issues in vision-based robotics, and self-driving. While previous works only focus on each task separately, we present an innovative and effi…

cs.CV2021

In Defense of Scene Graphs for Image Captioning

Kien Nguyen, Subarna Tripathi, Bang Du +2

The mainstream image captioning models rely on Convolutional Neural Network (CNN) image features to generate captions via recurrent models. Recently, image scene graphs have been u…

cs.CV2019★ 3 cited

Toward Joint Image Generation and Compression using Generative Adversarial Networks

Byeongkeun Kang, Subarna Tripathi, Truong Q. Nguyen

In this paper, we present a generative adversarial network framework that generates compressed images instead of synthesizing raw RGB images and compressing them separately. In the…

cs.CV2019★ 51 cited

Random Forest with Learned Representations for Semantic Segmentation

Byeongkeun Kang, Truong Q. Nguyen

In this work, we present a random forest framework that learns the weights, shapes, and sparsities of feature representations for real-time semantic segmentation. Typical filters (…

cs.CV2018

Accurate and efficient video de-fencing using convolutional neural networks and temporal information

Chen Du, Byeongkeun Kang, Zheng Xu +2

De-fencing is to eliminate the captured fence on an image or a video, providing a clear view of the scene. It has been applied for many purposes including assisting photographers a…

cs.CV2018

DPW-SDNet: Dual Pixel-Wavelet Domain Deep CNNs for Soft Decoding of JPEG-Compressed Images

Honggang Chen, Xiaohai He, Linbo Qing +2

JPEG is one of the widely used lossy compression methods. JPEG-compressed images usually suffer from compression artifacts including blocking and blurring, especially at low bit-ra…