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Found 23 papers, 18 papers with code
Read Pointer Meters in complex environments based on a Human-like Alignment and Recognition Algorithm
Recently, developing an automatic reading system for analog measuring instruments has gained increased attention, as it enables the collection of numerous state of equipment.
Wind Power Scenario Generation Using Graph Convolutional Generative Adversarial Network
Generating wind power scenarios is very important for studying the impacts of multiple wind farms that are interconnected to the grid.
Dynamic Response Recovery Using Ambient Synchrophasor Data: A Synthetic Texas Interconnection Case Study
Wide-area dynamic studies are of paramount importance to ensure the stability and reliability of power grids.
Fast Hierarchical Deep Unfolding Network for Image Compressed Sensing
By integrating certain optimization solvers with deep neural network, deep unfolding network (DUN) has attracted much attention in recent years for image compressed sensing (CS).
ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wild
In addition, our method is able to retain reasonable accuracy of camera poses on fully static scenes, which consistently outperforms strong state-of-the-art dense correspondence based methods with end-to-end deep learning, demonstrating the potential of dense indirect methods based on optical flow and point trajectories.
Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutions
We develop a new topology-informed graph neural network (GNN) approach for predicting the optimal solutions of real-time ac-OPF problem.
A Confidence-based Iterative Solver of Depths and Surface Normals for Deep Multi-view Stereo
Experimental results on ScanNet and RGB-D Scenes V2 demonstrate state-of-the-art performance of the proposed deep MVS system on multi-view depth estimation, with our proposed solver consistently improving the depth quality over both conventional and deep learning based MVS pipelines.
Image Compressed Sensing Using Non-local Neural Network
In this paper, a novel image CS framework using non-local neural network (NL-CSNet) is proposed, which utilizes the non-local self-similarity priors with deep network to improve the reconstruction quality.
Risk-Aware Learning for Scalable Voltage Optimization in Distribution Grids
Real-time coordination of distributed energy resources (DERs) is crucial for regulating the voltage profile in distribution grids.
NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo
In this work, we present a new multi-view depth estimation method that utilizes both conventional reconstruction and learning-based priors over the recently proposed neural radiance fields (NeRF).
Graph Neural Networks for Learning Real-Time Prices in Electricity Market
Solving the optimal power flow (OPF) problem in real-time electricity market improves the efficiency and reliability in the integration of low-carbon energy resources into the power grids.
Image Inpainting with Edge-guided Learnable Bidirectional Attention Maps
In this paper, we present an edge-guided learnable bidirectional attention map (Edge-LBAM) for improving image inpainting of irregular holes with several distinct merits.
A Dynamic Response Recovery Framework Using Ambient Synchrophasor Data
Wide-area dynamic studies are of paramount importance to ensure the stability and reliability of power grids.
Multi-Stage Residual Hiding for Image-into-Audio Steganography
The widespread application of audio communication technologies has speeded up audio data flowing across the Internet, which made it a popular carrier for covert communication.
Towards Better Generalization: Joint Depth-Pose Learning without PoseNet
In this work, we tackle the essential problem of scale inconsistency for self-supervised joint depth-pose learning.
DIST: Rendering Deep Implicit Signed Distance Function with Differentiable Sphere Tracing
We propose a differentiable sphere tracing algorithm to bridge the gap between inverse graphics methods and the recently proposed deep learning based implicit signed distance function.
Image Inpainting with Learnable Bidirectional Attention Maps
Most convolutional network (CNN)-based inpainting methods adopt standard convolution to indistinguishably treat valid pixels and holes, making them limited in handling irregular holes and more likely to generate inpainting results with color discrepancy and blurriness.
Ranked #2 on
Image Inpainting
on Paris StreetView
RepPoints: Point Set Representation for Object Detection
They furthermore do not require the use of anchors to sample a space of bounding boxes.
Ranked #82 on
Object Detection
on COCO minival
Conditional Single-view Shape Generation for Multi-view Stereo Reconstruction
In this paper, we present a new perspective towards image-based shape generation.
Normalized Diversification
We introduce the concept of normalized diversity which force the model to preserve the normalized pairwise distance between the sparse samples from a latent parametric distribution and their corresponding high-dimensional outputs.
An Improved Evaluation Framework for Generative Adversarial Networks
Unlike most existing evaluation frameworks which transfer the representation of ImageNet inception model to map images onto the feature space, our framework uses a specialized encoder to acquire fine-grained domain-specific representation.
Impression Network for Video Object Detection
Video object detection is more challenging compared to image object detection.
An End-to-End Compression Framework Based on Convolutional Neural Networks
The second CNN, named reconstruction convolutional neural network (RecCNN), is used to reconstruct the decoded image with high-quality in the decoding end.
