Search Results for author:
Found 36 papers, 22 papers with code
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.
Impression Network for Video Object Detection
Video object detection is more challenging compared to image object detection.
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.
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.
Conditional Image Generation
Generative Adversarial Network
+2
Conditional Single-view Shape Generation for Multi-view Stereo Reconstruction
In this paper, we present a new perspective towards image-based shape generation.
RepPoints: Point Set Representation for Object Detection
They furthermore do not require the use of anchors to sample a space of bounding boxes.
Ranked #95 on
Object Detection
on COCO minival
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
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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).
Source Camera Identification with Multi-Scale Feature Fusion Network
Subsequently, the content-independent SPN features of different scales are fused.
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.
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.
LNPL-MIL: Learning from Noisy Pseudo Labels for Promoting Multiple Instance Learning in Whole Slide Image
Gigapixel Whole Slide Images (WSIs) aided patient diagnosis and prognosis analysis are promising directions in computational pathology.
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.
How can Deep Learning Retrieve the Write-Missing Additional Diagnosis from Chinese Electronic Medical Record For DRG
Under the circumstance that countries generally start to adopt DRG enrollment and payment, the problem of write-missing diagnosis is a common and serious problem.
3D Line Mapping Revisited
In contrast to sparse keypoints, a handful of line segments can concisely encode the high-level scene layout, as they often delineate the main structural elements.
Exploring semantic information in disease: Simple Data Augmentation Techniques for Chinese Disease Normalization
Nevertheless, the most significant obstacle to existing disease name normalization systems is the severe shortage of training data.
Lazy Visual Localization via Motion Averaging
Visual (re)localization is critical for various applications in computer vision and robotics.
Vanishing Point Estimation in Uncalibrated Images with Prior Gravity Direction
We tackle the problem of estimating a Manhattan frame, i. e. three orthogonal vanishing points, and the unknown focal length of the camera, leveraging a prior vertical direction.
Handbook on Leveraging Lines for Two-View Relative Pose Estimation
We propose an approach for estimating the relative pose between calibrated image pairs by jointly exploiting points, lines, and their coincidences in a hybrid manner.
Data-driven Forced Oscillation Localization using Inferred Impulse Responses
Poorly damped oscillations pose threats to the stability and reliability of interconnected power systems.
CLiF-VQA: Enhancing Video Quality Assessment by Incorporating High-Level Semantic Information related to Human Feelings
In this paper, we propose CLiF-VQA, which considers both features related to human feelings and spatial features of videos.
Depth-agnostic Single Image Dehazing
To overcome the problem, we propose a simple yet novel synthetic method to decouple the relationship between haze density and scene depth, by which a depth-agnostic dataset (DA-HAZE) is generated.
Deep Network for Image Compressed Sensing Coding Using Local Structural Sampling
In this paper, we propose a new CNN based image CS coding framework using local structural sampling (dubbed CSCNet) that includes three functional modules: local structural sampling, measurement coding and Laplacian pyramid reconstruction.
SC-HVPPNet: Spatial and Channel Hybrid-Attention Video Post-Processing Network with CNN and Transformer
Convolutional Neural Network (CNN) and Transformer have attracted much attention recently for video post-processing (VPP).
