27 citations · 70 across the 15 of their papers we have counts for
5 papers · 1 filter
Multi-Class Lane Semantic Segmentation using Efficient Convolutional Networks
Shao-Yuan Lo, Hsueh-Ming Hang, Sheng-Wei Chan +1
Lane detection plays an important role in a self-driving vehicle. Several studies leverage a semantic segmentation network to extract robust lane features, but few of them can dist…
Exploring Semantic Segmentation on the DCT Representation
Shao-Yuan Lo, Hsueh-Ming Hang
Typical convolutional networks are trained and conducted on RGB images. However, images are often compressed for memory savings and efficient transmission in real-world application…
Learned Image Compression with Soft Bit-based Rate-Distortion Optimization
David Alexandre, Chih-Peng Chang, Wen-Hsiao Peng +1
This paper introduces the notion of soft bits to address the rate-distortion optimization for learning-based image compression. Recent methods for such compression train an autoenc…
DSNet: An Efficient CNN for Road Scene Segmentation
Ping-Rong Chen, Hsueh-Ming Hang, Sheng-Wei Chan +1
Road scene understanding is a critical component in an autonomous driving system. Although the deep learning-based road scene segmentation can achieve very high accuracy, its compl…
An Autoencoder-based Learned Image Compressor: Description of Challenge Proposal by NCTU
David Alexandre, Chih-Peng Chang, Wen-Hsiao Peng +1
We propose a lossy image compression system using the deep-learning autoencoder structure to participate in the Challenge on Learned Image Compression (CLIC) 2018. Our autoencoder…