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Found 16 papers, 5 papers with code
Hierarchical B-frame Video Coding Using Two-Layer CANF without Motion Coding
The rate-distortion performance of our scheme is slightly lower than that of the state-of-the-art learned B-frame coding scheme, B-CANF, but outperforms other learned B-frame coding schemes.
Learned Hierarchical B-frame Coding with Adaptive Feature Modulation for YUV 4:2:0 Content
Most learned video codecs operate internally in the RGB domain for P-frame coding.
Learned Video Compression for YUV 4:2:0 Content Using Flow-based Conditional Inter-frame Coding
To adapt our codec to YUV 4:2:0 content, we adopt a simple strategy of using space-to-depth and depth-to-space conversions.
Fast Vehicle Detection and Tracking on Fisheye Traffic Monitoring Video using CNN and Bounding Box Propagation
We design a fast car detection and tracking algorithm for traffic monitoring fisheye video mounted on crossroads.
ANFIC: Image Compression Using Augmented Normalizing Flows
This paper introduces an end-to-end learned image compression system, termed ANFIC, based on Augmented Normalizing Flows (ANF).
FINED: Fast Inference Network for Edge Detection
In contrast, we propose a Fast Inference Network for Edge Detection (FINED), which is a lightweight neural net dedicated to edge detection.
Learned Video Codec with Enriched Reconstruction for CLIC P-frame Coding
More specifically, we designed a compressor network with Refine-Net for coding residual signals and motion vectors.
Traditional Method Inspired Deep Neural Network for Edge Detection
Therefore, we propose a traditional method inspired framework to produce good edges with minimal complexity.
Semi-supervised Semantic Segmentation using Auxiliary Network
Then, in the unsupervised training phase, the unlabeled images are segmented and a subset of image pixels are picked up by the auxiliary network; and then they are used as ground truth to train the segmentation network.
Exploring Semantic Segmentation on the DCT Representation
The proposed method has an accuracy close to the RGB model at about the same network complexity.
Ranked #89 on
Semantic Segmentation
on Cityscapes val
Multi-Class Lane Semantic Segmentation using Efficient Convolutional Networks
Several studies leverage a semantic segmentation network to extract robust lane features, but few of them can distinguish different types of lanes.
DSNet: An Efficient CNN for Road Scene Segmentation
Although the deep learning-based road scene segmentation can achieve very high accuracy, its complexity is also very high for developing real-time applications.
An Autoencoder-based Learned Image Compressor: Description of Challenge Proposal by NCTU
We propose a lossy image compression system using the deep-learning autoencoder structure to participate in the Challenge on Learned Image Compression (CLIC) 2018.
Incorporating Luminance, Depth and Color Information by a Fusion-based Network for Semantic Segmentation
It includes a sub-network to process depth maps and employs luminance images to assist the depth information in processes.
Ranked #23 on
Real-Time Semantic Segmentation
on Cityscapes val
Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation
Real-time semantic segmentation plays an important role in practical applications such as self-driving and robots.
Ranked #13 on
Semantic Segmentation
on CamVid
Efficient Road Lane Marking Detection with Deep Learning
Lane mark detection is an important element in the road scene analysis for Advanced Driver Assistant System (ADAS).
Ranked #17 on
Semantic Segmentation
on CamVid
