Search Results for author:
Found 14 papers, 3 papers with code
Real-Time Super-Resolution System of 4K-Video Based on Deep Learning
In order to pursue faster VSR processing ability up to 4K resolution, this paper tries to choose lightweight network structure and efficient upsampling method to reduce the computation required by EGVSR network under the guarantee of high visual quality.
Ranked #2 on
Video Super-Resolution
on MSU Video Upscalers: Quality Enhancement
(VMAF metric)
Uncertainty-Aware Unsupervised Domain Adaptation in Object Detection
Specifically, we design an uncertainty metric that assesses the alignment of each sample and adjusts the strength of adversarial learning for well-aligned and poorly-aligned samples adaptively.
Learning Inter- and Intraframe Representations for Non-Lambertian Photometric Stereo
Photometric stereo provides an important method for high-fidelity 3D reconstruction based on multiple intensity images captured under different illumination directions.
LGENet: Local and Global Encoder Network for Semantic Segmentation of Airborne Laser Scanning Point Clouds
Interpretation of Airborne Laser Scanning (ALS) point clouds is a critical procedure for producing various geo-information products like 3D city models, digital terrain models and land use maps.
Boosting Image Super-Resolution Via Fusion of Complementary Information Captured by Multi-Modal Sensors
Image Super-Resolution (SR) provides a promising technique to enhance the image quality of low-resolution optical sensors, facilitating better-performing target detection and autonomous navigation in a wide range of robotics applications.
Few-Shot Defect Segmentation Leveraging Abundant Normal Training Samples Through Normal Background Regularization and Crop-and-Paste Operation
In industrial inspection tasks, it is common to capture abundant defect-free image samples but very limited anomalous ones.
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+.
Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features
Firstly, we proposed a novel Orientation-Aware feature extraction and fusion Module (OAM), which contains a mixture of 1D and 2D convolutional kernels (i. e., 5 x 1, 1 x 5, and 3 x 3) for extracting orientation-aware features.
A deep-learning-based approach for fast and robust steel surface defects classification
Automatic visual recognition of steel surface defects provides critical functionality to facilitate quality control of steel strip production.
Unsupervised Domain Adaptation for Multispectral Pedestrian Detection
In this paper, we propose a novel unsupervised domain adaptation framework for multispectral pedestrian detection, by iteratively generating pseudo annotations and updating the parameters of our designed multispectral pedestrian detector on target domain.
Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection
Effective fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situations (e. g. daytime and nighttime).
Security Event Recognition for Visual Surveillance
The experimental results show that the proposed approach outperforms the state-of-the-art methods and effective in recognizing complex security events.
Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection
Moreover, we utilized illumination information together with multispectral data to generate more accurate semantic segmentation which are used to boost pedestrian detection accuracy.
Video Event Recognition and Anomaly Detection by Combining Gaussian Process and Hierarchical Dirichlet Process Models
In our framework, three levels of video events are connected by Hierarchical Dirichlet Process (HDP) model: low-level visual features, simple atomic activities, and multi-agent interactions.
