74 citations · 128 across the 4 of their papers we have counts for
6 papers
Incremental Class Discovery for Semantic Segmentation with RGBD Sensing
Yoshikatsu Nakajima, Byeongkeun Kang, Hideo Saito +1
This work addresses the task of open world semantic segmentation using RGBD sensing to discover new semantic classes over time. Although there are many types of objects in the real…
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…
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 (…
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…
Depth Adaptive Deep Neural Network for Semantic Segmentation
Byeongkeun Kang, Yeejin Lee, Truong Q. Nguyen
In this work, we present the depth-adaptive deep neural network using a depth map for semantic segmentation. Typical deep neural networks receive inputs at the predetermined locati…
LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems
Subarna Tripathi, Gokce Dane, Byeongkeun Kang +2
Deep convolutional Neural Networks (CNN) are the state-of-the-art performers for object detection task. It is well known that object detection requires more computation and memory…