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Found 16 papers, 4 papers with code
Evaluating Modern Approaches in 3D Scene Reconstruction: NeRF vs Gaussian-Based Methods
Exploring the capabilities of Neural Radiance Fields (NeRF) and Gaussian-based methods in the context of 3D scene reconstruction, this study contrasts these modern approaches with traditional Simultaneous Localization and Mapping (SLAM) systems.
Physical Data Embedding for Memory Efficient AI
The proposed method of replacing traditional DNN feature learning architectures with physical equations is also extended to the Gross-Pitaevskii Equation, demonstrating the broad applicability of the framework to other master equations of physics.
Mapping New Realities: Ground Truth Image Creation with Pix2Pix Image-to-Image Translation
Generative Adversarial Networks (GANs) have significantly advanced image processing, with Pix2Pix being a notable framework for image-to-image translation.
Integrating AI in NDE: Techniques, Trends, and Further Directions
Since we cannot discuss each NDE modality in one paper, we limit our attention to magnetic methods, ultrasound, thermography, as well as optical inspection.
Jointly Learning Selection Matrices For Transmitters, Receivers And Fourier Coefficients In Multichannel Imaging
Strategic subsampling has become a focal point due to its effectiveness in compressing data, particularly in the Full Matrix Capture (FMC) approach in ultrasonic imaging.
Communication-Efficient Distributed Learning with Local Immediate Error Compensation
However, existing compression methods either perform only unidirectional compression in one iteration with higher communication cost, or bidirectional compression with slower convergence rate.
DDN-SLAM: Real-time Dense Dynamic Neural Implicit SLAM
To address dynamic tracking interferences, we propose a feature point segmentation method that combines semantic features with a mixed Gaussian distribution model.
Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks
This paper explores the application of convolutional neural networks for the purpose of multimodal deception detection.
A Multi-Source Data Fusion-based Semantic Segmentation Model for Relic Landslide Detection
To extract accurate semantic features, a hyper-pixel-wise contrastive learning augmented segmentation network (HPCL-Net) is proposed, which augments the local salient feature extraction from boundaries of landslides through HPCL and fuses heterogeneous information in the semantic space from high-resolution remote sensing images and digital elevation model data.
SimpleNet: A Simple Network for Image Anomaly Detection and Localization
SimpleNet consists of four components: (1) a pre-trained Feature Extractor that generates local features, (2) a shallow Feature Adapter that transfers local features towards target domain, (3) a simple Anomaly Feature Generator that counterfeits anomaly features by adding Gaussian noise to normal features, and (4) a binary Anomaly Discriminator that distinguishes anomaly features from normal features.
Ranked #3 on
Anomaly Classification
on GoodsAD
PhyCV: The First Physics-inspired Computer Vision Library
PhyCV is the first computer vision library which utilizes algorithms directly derived from the equations of physics governing physical phenomena.
Improving Levenberg-Marquardt Algorithm for Neural Networks
We explore the usage of the Levenberg-Marquardt (LM) algorithm for regression (non-linear least squares) and classification (generalized Gauss-Newton methods) tasks in neural networks.
Deep Analog-to-Digital Converter for Wireless Communication
We demonstrate this "Deep ADC" technique on an 8G Sample/s 8-channel time-interleaved ADC with the QAM-OFDM modulated data.
Deep Learning Interference Cancellation in Wireless Networks
In this paper, we propose a Neural Network (NN) based signal processing technique that works with traditional DSP algorithms to overcome the interference problem in realtime.
Accuracy and Resiliency of Analog Compute-in-Memory Inference Engines
To explore this critical issue of scalability, this paper first presents a simulation framework to evaluate the feasibility of large-scale DNNs based on CIM architecture and analog NVM.
Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search
It prunes the architecture search space with a partial order assumption to automatically search for the architectures with the best speed and accuracy trade-off.
Ranked #12 on
Semantic Segmentation
on BDD100K val
