Search Results for author:
Found 25 papers, 5 papers with code
Samba: Semantic Segmentation of Remotely Sensed Images with State Space Model
The results reveal that Samba achieved unparalleled performance on commonly used remote sensing datasets for semantic segmentation.
Comparing roughness maps generated by five roughness descriptors for LiDAR-derived digital elevation models
Terrain surface roughness, often described abstractly, poses challenges in quantitative characterisation with various descriptors found in the literature.
Active Open-Vocabulary Recognition: Let Intelligent Moving Mitigate CLIP Limitations
Active recognition, which allows intelligent agents to explore observations for better recognition performance, serves as a prerequisite for various embodied AI tasks, such as grasping, navigation and room arrangements.
Evidential Active Recognition: Intelligent and Prudent Open-World Embodied Perception
Active recognition enables robots to intelligently explore novel observations, thereby acquiring more information while circumventing undesired viewing conditions.
An annotated grain kernel image database for visual quality inspection
We present a machine vision-based database named GrainSet for the purpose of visual quality inspection of grain kernels.
Identifying the Defective: Detecting Damaged Grains for Cereal Appearance Inspection
Cereal grain plays a crucial role in the human diet as a major source of essential nutrients.
PolicyGPT: Automated Analysis of Privacy Policies with Large Language Models
In this study, we investigate a privacy policy text analysis framework PolicyGPT based on the LLM.
Flexible Visual Recognition by Evidential Modeling of Confusion and Ignorance
First, prediction uncertainty should be separately quantified as confusion depicting inter-class uncertainties and ignorance identifying out-of-distribution samples.
Selection of contributing factors for predicting landslide susceptibility using machine learning and deep learning models
In this study, the impact of the selection of contributing factors on the accuracy of landslide susceptibility predictions using ML and DL models was investigated.
Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey
Deep learning (DL) has become one of the mainstream and effective methods for point cloud analysis tasks such as detection, segmentation and classification.
SDRCNN: A single-scale dense residual connected convolutional neural network for pansharpening
Pansharpening is a process of fusing a high spatial resolution panchromatic image and a low spatial resolution multispectral image to create a high-resolution multispectral image.
Deep Learning-based Prediction of Electrical Arrhythmia Circuits from Cardiac Motion: An In-Silico Study
We performed thousands of simulations of electromechanical activation dynamics in ventricular geometries and used the data to train a neural network which subsequently predicts the three-dimensional electrical wave pattern that caused the deformation.
OSTA: One-shot Task-adaptive Channel Selection for Semantic Segmentation of Multichannel Images
In this study, the concept of pruning from a supernet is used for the first time to integrate the selection of channel combination and the training of a semantic segmentation network.
Deformation measurement of a soil mixing retaining wall using terrestrial laser scanning
Retaining walls are often built to prevent excessive lateral movements of the ground surrounding an excavation site.
SBSS: Stacking-Based Semantic Segmentation Framework for Very High Resolution Remote Sensing Image
However, it was found in this study that different classes of objects had their preferred resizing scale for more accurate semantic segmentation.
GrainSpace: A Large-scale Dataset for Fine-grained and Domain-adaptive Recognition of Cereal Grains
To the best of our knowledge, GrainSpace is the first publicly released dataset for cereal grain inspection.
Learning to Help Emergency Vehicles Arrive Faster: A Cooperative Vehicle-Road Scheduling Approach
The ever-increasing heavy traffic congestion potentially impedes the accessibility of emergency vehicles (EVs), resulting in detrimental impacts on critical services and even safety of people's lives.
Practical Adoption of Cloud Computing in Power Systems- Drivers, Challenges, Guidance, and Real-world Use Cases
Motivated by The Federal Energy Regulatory Commission's (FERC) recent direction and ever-growing interest in cloud adoption by power utilities, a Task Force was established to assist power system practitioners with secure, reliable and cost-effective adoption of cloud technology to meet various business needs.
Minimizing Delay in Network Function Visualization with Quantum Computing
There are vast service chains in NFV to meet users' requests, which are composed of a sequence of network functions.
FLAR: A Unified Prototype Framework for Few-Sample Lifelong Active Recognition
To address this demand, in this paper, we propose a unified framework towards Few-sample Lifelong Active Recognition (FLAR), which aims at performing active recognition on progressively arising novel categories that only have few training samples.
Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature Learning
In this paper, we develop a computation efficient yet accurate network based on the proposed attentive auxiliary features (A$^2$F) for SISR.
Combining Visible Light and Infrared Imaging for Efficient Detection of Respiratory Infections such as COVID-19 on Portable Device
In this work, we perform the health screening through the combination of the RGB and thermal videos obtained from the dual-mode camera and deep learning architecture. We first accomplish a respiratory data capture technique for people wearing masks by using face recognition.
Accurate Frequency Estimator of Sinusoid Based on Interpolation of FFT and DTFT
The correlation coef cients between the Fourier Transform of the noises on two arbitrarily spaced spectrum lines are derived, and the MSE calculation formula is derived in additive white noise background based on the correlation coef cients.
Monocular Outdoor Semantic Mapping with a Multi-task Network
First, with the correlation of underlying information between depth and semantic prediction, a novel multi-task Convolutional Neural Network (CNN) is designed for joint prediction.
Planecell: Representing the 3D Space with Planes
Reconstruction based on the stereo camera has received considerable attention recently, but two particular challenges still remain.
