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Found 25 papers, 5 papers with code
CSST Strong Lensing Preparation: a Framework for Detecting Strong Lenses in the Multi-color Imaging Survey by the China Survey Space Telescope (CSST)
To overcome these challenges, we have developed a framework based on a hierarchical visual Transformer with a sliding window technique to search for strong lensing systems within entire images.
TOD3Cap: Towards 3D Dense Captioning in Outdoor Scenes
However, the exploration of 3D dense captioning in outdoor scenes is hindered by two major challenges: 1) the \textbf{domain gap} between indoor and outdoor scenes, such as dynamics and sparse visual inputs, makes it difficult to directly adapt existing indoor methods; 2) the \textbf{lack of data} with comprehensive box-caption pair annotations specifically tailored for outdoor scenes.
A Data-Driven Approach for Mitigating Dark Current Noise and Bad Pixels in Complementary Metal Oxide Semiconductor Cameras for Space-based Telescopes
In this paper, we introduce a data-driven framework for mitigating dark current noise and bad pixels for CMOS cameras.
DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models
We introduce DriveVLM, an autonomous driving system leveraging Vision-Language Models (VLMs) for enhanced scene understanding and planning capabilities.
PC-NeRF: Parent-Child Neural Radiance Fields Using Sparse LiDAR Frames in Autonomous Driving Environments
With extensive experiments, PC-NeRF is proven to achieve high-precision novel LiDAR view synthesis and 3D reconstruction in large-scale scenes.
BEV-CLIP: Multi-modal BEV Retrieval Methodology for Complex Scene in Autonomous Driving
The demand for the retrieval of complex scene data in autonomous driving is increasing, especially as passenger vehicles have been equipped with the ability to navigate urban settings, with the imperative to address long-tail scenarios.
Guidelines in Wastewater-based Epidemiology of SARS-CoV-2 with Diagnosis
With the global spread and increasing transmission rate of SARS-CoV-2, more and more laboratories and researchers are turning their attention to wastewater-based epidemiology (WBE), hoping it can become an effective tool for large-scale testing and provide more ac-curate predictions of the number of infected individuals.
Perception of Misalignment States for Sky Survey Telescopes with the Digital Twin and the Deep Neural Networks
To address this, we need a method to obtain misalignment states, aiding in the reconstruction of accurate point spread functions for data processing methods or facilitating adjustments of optical components for improved image quality.
Image Restoration with Point Spread Function Regularization and Active Learning
Large-scale astronomical surveys can capture numerous images of celestial objects, including galaxies and nebulae.
PC-NeRF: Parent-Child Neural Radiance Fields under Partial Sensor Data Loss in Autonomous Driving Environments
Reconstructing large-scale 3D scenes is essential for autonomous vehicles, especially when partial sensor data is lost.
Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine Learning Algorithms
Then an algorithm based on morphological operations and two neural networks would be used to detect candidates of celestial objects with different flux from these 2D images.
Detection of Strongly Lensed Arcs in Galaxy Clusters with Transformers
We embed prior information of strongly lensed arcs at cluster-scale into the training data through simulation and then train the detection algorithm with simulated images.
Reinforcement Learning for Few-Shot Text Generation Adaptation
Controlling the generative model to adapt a new domain with limited samples is a difficult challenge and it is receiving increasing attention.
PNet -- A Deep Learning Based Photometry and Astrometry Bayesian Framework
Time domain astronomy has emerged as a vibrant research field in recent years, focusing on celestial objects that exhibit variable magnitudes or positions.
Compressive Shack-Hartmann Wavefront Sensor based on Deep Neural Networks
However if there exists strong atmospheric turbulence or the brightness of guide stars is low, the accuracy of wavefront measurements will be affected.
Smart obervation method with wide field small aperture telescopes for real time transient detection
The ARGUS uses a deep learning based astronomical detection algorithm implemented in embedded devices in each WFSATs to detect astronomical targets.
Point Spread Function Estimation for Wide Field Small Aperture Telescopes with Deep Neural Networks and Calibration Data
Then we use these data to train a DNN (Tel--Net).
Data--driven Image Restoration with Option--driven Learning for Big and Small Astronomical Image Datasets
In recent years, developments of deep neural networks and increments of the number of astronomical images have evoked a lot of data--driven image restoration methods.
PSF--NET: A Non-parametric Point Spread Function Model for Ground Based Optical Telescopes
Assuming point spread functions induced by the atmospheric turbulence with the same profile belong to the same manifold space, we propose a non-parametric point spread function -- PSF-NET.
Detection and Classification of Astronomical Targets with Deep Neural Networks in Wide Field Small Aperture Telescopes
To increase the generalization ability of our framework, we use both simulated and real observation images to train the neural network.
Point Spread Function Modelling for Wide Field Small Aperture Telescopes with a Denoising Autoencoder
For wide field small aperture telescopes, the point spread function is hard to model, because it is affected by many different effects and has strong temporal and spatial variations.
A systematic review of fuzzing based on machine learning techniques
Next, this paper assesses the performance of the machine learning models based on the frequently used evaluation metrics.
Solar Image Restoration with the Cycle-GAN Based on Multi-Fractal Properties of Texture Features
Texture is one of the most obvious characteristics in solar images and it is normally described by texture features.
Perception Evaluation -- A new solar image quality metric based on the multi-fractal property of texture features
Next-generation ground-based solar observations require good image quality metrics for post-facto processing techniques.
Optical Transient Object Classification in Wide Field Small Aperture Telescopes with Neural Networks
In this paper, we propose two transient classification methods based on neural networks.
