27 citations · 70 across the 15 of their papers we have counts for
15 papers
Hierarchical B-frame Video Coding Using Two-Layer CANF without Motion Coding
David Alexandre, Hsueh-Ming Hang, Wen-Hsiao Peng
Typical video compression systems consist of two main modules: motion coding and residual coding. This general architecture is adopted by classical coding schemes (such as internat…
Learned Hierarchical B-frame Coding with Adaptive Feature Modulation for YUV 4:2:0 Content
Mu-Jung Chen, Hong-Sheng Xie, Cheng Chien +2
This paper introduces a learned hierarchical B-frame coding scheme in response to the Grand Challenge on Neural Network-based Video Coding at ISCAS 2023. We address specifically th…
Learned Video Compression for YUV 4:2:0 Content Using Flow-based Conditional Inter-frame Coding
Yung-Han Ho, Chih-Hsuan Lin, Peng-Yu Chen +4
This paper proposes a learning-based video compression framework for variable-rate coding on YUV 4:2:0 content. Most existing learning-based video compression models adopt the trad…
Fast Vehicle Detection and Tracking on Fisheye Traffic Monitoring Video using CNN and Bounding Box Propagation
Sandy Ardianto, Hsueh-Ming Hang, Wen-Huang Cheng
We design a fast car detection and tracking algorithm for traffic monitoring fisheye video mounted on crossroads. We use ICIP 2020 VIP Cup dataset and adopt YOLOv5 as the object de…
ANFIC: Image Compression Using Augmented Normalizing Flows
Yung-Han Ho, Chih-Chun Chan, Wen-Hsiao Peng +2
This paper introduces an end-to-end learned image compression system, termed ANFIC, based on Augmented Normalizing Flows (ANF). ANF is a new type of flow model, which stacks multip…
FINED: Fast Inference Network for Edge Detection
Jan Kristanto Wibisono, Hsueh-Ming Hang
In this paper, we address the design of lightweight deep learning-based edge detection. The deep learning technology offers a significant improvement on the edge detection accuracy…
Learned Video Codec with Enriched Reconstruction for CLIC P-frame Coding
David Alexandre, Hsueh-Ming Hang
This paper proposes a learning-based video codec, specifically used for Challenge on Learned Image Compression (CLIC, CVPRWorkshop) 2020 P-frame coding. More specifically, we desig…
Traditional Method Inspired Deep Neural Network for Edge Detection
Jan Kristanto Wibisono, Hsueh-Ming Hang
Recently, Deep-Neural-Network (DNN) based edge prediction is progressing fast. Although the DNN based schemes outperform the traditional edge detectors, they have much higher compu…
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…
Efficient Road Lane Marking Detection with Deep Learning
Ping-Rong Chen, Shao-Yuan Lo, Hsueh-Ming Hang +2
Lane mark detection is an important element in the road scene analysis for Advanced Driver Assistant System (ADAS). Limited by the onboard computing power, it is still a challenge…
Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation
Shao-Yuan Lo, Hsueh-Ming Hang, Sheng-Wei Chan +1
Real-time semantic segmentation plays an important role in practical applications such as self-driving and robots. Most semantic segmentation research focuses on improving estimati…