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Found 150 papers, 48 papers with code
Binarized Neural Network for Single Image Super Resolution
Lighter model and faster inference are the focus of current single image super-resolution (SISR) research.
The University of Maryland’s Submissions to the WMT20 Chat Translation Task: Searching for More Data to Adapt Discourse-Aware Neural Machine Translation
This paper describes the University of Maryland’s submissions to the WMT20 Shared Task on Chat Translation.
Predicting Molecule-Target Interaction by Learning Biomedical Network and Molecule Representations
Most existing methodologies utilize either biomedical network information or molecule structural features to predict potential interaction link.
Multi-scale multi-modal micro-expression recognition algorithm based on transformer
A micro-expression is a spontaneous unconscious facial muscle movement that can reveal the true emotions people attempt to hide.
Breaking the "Object" in Video Object Segmentation
Yet, this important phenomenon is largely absent from existing video object segmentation (VOS) benchmarks.
ShaSTA: Modeling Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking
This can negatively affect the tracking component by making data association and track lifecycle management more challenging.
Automatic Change-Point Detection in Time Series via Deep Learning
We show how to automatically generate new detection methods based on training a neural network.
Reconstruction of compressed spectral imaging based on global structure and spectral correlation
To take full exploration of the constraints between spectra, the coefficients corresponding to the convolution kernel are constrained by the L_(2, 1)norm to improve spectral accuracy.
Depth Is All You Need for Monocular 3D Detection
Our methods leverage commonly available LiDAR or RGB videos during training time to fine-tune the depth representation, which leads to improved 3D detectors.
Seen to Unseen: When Fuzzy Inference System Predicts IoT Device Positioning Labels That Had Not Appeared in Training Phase
Situating at the core of Artificial Intelligence (AI), Machine Learning (ML), and more specifically, Deep Learning (DL) have embraced great success in the past two decades.
Parameter-Efficient Conformers via Sharing Sparsely-Gated Experts for End-to-End Speech Recognition
Experimental results show that the proposed model achieves competitive performance with 1/3 of the parameters of the encoder, compared with the full-parameter model.
Seeking Subjectivity in Visual Emotion Distribution Learning
In psychology, the \textit{Object-Appraisal-Emotion} model has demonstrated that each individual's emotion is affected by his/her subjective appraisal, which is further formed by the affective memory.
SpOT: Spatiotemporal Modeling for 3D Object Tracking
In this work, we develop a holistic representation of traffic scenes that leverages both spatial and temporal information of the actors in the scene.
TransFA: Transformer-based Representation for Face Attribute Evaluation
The multiple branches transformer is employed to explore the inter-correlation between different attributes in similar semantic regions for attribute feature learning.
Digital-twin-enhanced metal tube bending forming real-time prediction method based on Multi-source-input MTL
To address this issue, a digital-twin-enhanced (DT-enhanced) metal tube bending forming real-time prediction method based on multi-source-input multi-task learning (MTL) is proposed.
Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?
Building 3D perception systems for autonomous vehicles that do not rely on high-density LiDAR is a critical research problem because of the expense of LiDAR systems compared to cameras and other sensors.
An Indoor Environment Sensing and Localization System via mmWave Phased Array
At the end, we show that the reconstructed room layout can be utilized to locate a mobile device according to its AoA spectrum, even with single access point.
FairGAN: GANs-based Fairness-aware Learning for Recommendations with Implicit Feedback
To fill this gap, we propose a Generative Adversarial Networks (GANs) based learning algorithm FairGAN mapping the exposure fairness issue to the problem of negative preferences in implicit feedback data.
Object Permanence Emerges in a Random Walk along Memory
This paper proposes a self-supervised objective for learning representations that localize objects under occlusion - a property known as object permanence.
Passive Motion Detection via mmWave Communication System
Relying on the passive sensing system, a dataset of received signals, where three types of hand gestures are sensed, is collected by using Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) paths as the reference channel respectively.
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach
Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations.
Spherical Convolution empowered FoV Prediction in 360-degree Video Multicast with Limited FoV Feedback
Most of the current prediction methods combining saliency detection and FoV information neither take into account that the distortion of projected 360-degree videos can invalidate the weight sharing of traditional convolutional networks, nor do they adequately consider the difficulty of obtaining complete multi-user FoV information, which degrades the prediction performance.
One-Bit Active Query With Contrastive Pairs
The Yes query is treated as positive pairs of the queried category for contrastive pulling, while the No query is treated as hard negative pairs for contrastive repelling.
Learning to Learn Transferable Attack
By treating the attack of both specific data and a modified model as a task, we expect the adversarial perturbations to adopt enough tasks for generalization.
Fully Attentional Network for Semantic Segmentation
Recent non-local self-attention methods have proven to be effective in capturing long-range dependencies for semantic segmentation.
Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides
Conclusion: Our study provides a novel DL-based biomarker on primary tumor CNB slides to predict the metastatic status of ALN preoperatively for patients with EBC.
Image-specific Convolutional Kernel Modulation for Single Image Super-resolution
Recently, deep-learning-based super-resolution methods have achieved excellent performances, but mainly focus on training a single generalized deep network by feeding numerous samples.
Denoised Non-Local Neural Network for Semantic Segmentation
Without any external training data, our proposed Denoised NL can achieve the state-of-the-art performance of 83. 5\% and 46. 69\% mIoU on Cityscapes and ADE20K, respectively.
Bone Marrow Cell Recognition: Training Deep Object Detection with A New Loss Function
Therefore, it is crucial to study a robust bone marrow cell detection algorithm for a quantitative automatic analysis system.
Reinforcement Learning with Real-time Docking of 3D Structures to Cover Chemical Space: Mining for Potent SARS-CoV-2 Main Protease Inhibitors
We propose a novel framework that generates new inhibitor molecules for target proteins by combining deep reinforcement learning (RL) with real-time molecular docking on 3-dimensional structures.
FedIPR: Ownership Verification for Federated Deep Neural Network Models
To address these risks, the ownership verification of federated learning models is a prerequisite that protects federated learning model intellectual property rights (IPR) i. e., FedIPR.
Geometry-Based Stochastic Line-of-Sight Probability Model for A2G Channels under Urban Scenarios
Line-of-sight (LoS) path is essential for the reliability of air-to-ground (A2G) communications, but the existence of LoS path is difficult to predict due to random obstacles on the ground.
Stimuli-Aware Visual Emotion Analysis
Then, we design three specific networks, i. e., Global-Net, Semantic-Net and Expression-Net, to extract distinct emotional features from different stimuli simultaneously.
An Integrated Framework for the Heterogeneous Spatio-Spectral-Temporal Fusion of Remote Sensing Images
Images from many remote sensing satellites, including MODIS, Landsat-8, Sentinel-1, and Sentinel-2, are utilized in the experiments.
LocTex: Learning Data-Efficient Visual Representations from Localized Textual Supervision
Computer vision tasks such as object detection and semantic/instance segmentation rely on the painstaking annotation of large training datasets.
Is Pseudo-Lidar needed for Monocular 3D Object detection?
Recent progress in 3D object detection from single images leverages monocular depth estimation as a way to produce 3D pointclouds, turning cameras into pseudo-lidar sensors.
Ranked #1 on
Monocular 3D Object Detection
on KITTI Pedestrian Hard
(using extra training data)
Coupling Model-Driven and Data-Driven Methods for Remote Sensing Image Restoration and Fusion
In this paper, we systematically investigate the coupling of model-driven and data-driven methods, which has rarely been considered in the remote sensing image restoration and fusion communities.
A Dynamic 3D Spontaneous Micro-expression Database: Establishment and Evaluation
Micro-expressions are spontaneous, unconscious facial movements that show people's true inner emotions and have great potential in related fields of psychological testing.
Real-time Keypoints Detection for Autonomous Recovery of the Unmanned Ground Vehicle
UGV-KPNet is computationally efficient with a small number of parameters and provides pixel-level accurate keypoints detection results in real-time.
Wideband photonic blind source separation with optical pulse sampling
The ultra-fast optical pulse functions as a tweezer that collects samples of the signals at very low sampling rates, and each sample is short enough to maintain the statistical properties of the signals.
Integrated Sensing and Communication from Learning Perspective: An SDP3 Approach
Characterizing the sensing and communication performance tradeoff in integrated sensing and communication (ISAC) systems is challenging in the applications of learning-based human motion recognition.
Fully Polarimetric SAR and Single-Polarization SAR Image Fusion Network
The data fusion technology aims to aggregate the characteristics of different data and obtain products with multiple data advantages.
IMENet: Joint 3D Semantic Scene Completion and 2D Semantic Segmentation through Iterative Mutual Enhancement
3D semantic scene completion and 2D semantic segmentation are two tightly correlated tasks that are both essential for indoor scene understanding, because they predict the same semantic classes, using positively correlated high-level features.
A Circular-Structured Representation for Visual Emotion Distribution Learning
Visual Emotion Analysis (VEA) has attracted increasing attention recently with the prevalence of sharing images on social networks.
GPLA-12: An Acoustic Signal Dataset of Gas Pipeline Leakage
In this paper, we introduce a new acoustic leakage dataset of gas pipelines, called as GPLA-12, which has 12 categories over 684 training/testing acoustic signals.
Learning the Non-Differentiable Optimization for Blind Super-Resolution
Instead of considering iterative strategy, we make the blur kernel predictor trainable in the whole blind SR model, in which AMNet is well-trained.
Inverse Simulation: Reconstructing Dynamic Geometry of Clothed Humans via Optimal Control
Inspired by the theory of optimal control, we optimize the body states such that the simulated cloth motion is matched to the point cloud measurements, and the analytic gradient of the simulator is back-propagated to update the body states.
Hierarchical Lovasz Embeddings for Proposal-Free Panoptic Segmentation
We use a hierarchical Lovasz hinge loss to learn a low-dimensional embedding space structured into a unified semantic and instance hierarchy without requiring separate network branches or object proposals.
Hierarchical Lovász Embeddings for Proposal-free Panoptic Segmentation
We use a hierarchical Lov\'asz hinge loss to learn a low-dimensional embedding space structured into a unified semantic and instance hierarchy without requiring separate network branches or object proposals.
Semi-supervised Contrastive Learning with Similarity Co-calibration
Semi-supervised learning acts as an effective way to leverage massive unlabeled data.
Black-Box Dissector: Towards Erasing-based Hard-Label Model Stealing Attack
We argue this is due to the lack of rich information in the probability prediction and the overfitting caused by hard labels.
Exploring Multi-dimensional Data via Subset Embedding
The core of the approach is a subset embedding network (SEN) that represents a group of subsets as uniformly-formatted embeddings.
Wireless Sensing With Deep Spectrogram Network and Primitive Based Autoregressive Hybrid Channel Model
Although wireless channel models can be adopted for dataset generation, current channel models are mostly designed for communication rather than sensing.
Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer
Inspired by the common painting process of drawing a draft and revising the details, we introduce a novel feed-forward method named Laplacian Pyramid Network (LapStyle).
Geometric Unsupervised Domain Adaptation for Semantic Segmentation
Simulators can efficiently generate large amounts of labeled synthetic data with perfect supervision for hard-to-label tasks like semantic segmentation.
Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolution
Being extremely dependent on iterative estimation of the degradation prior or optimization of the model from scratch, the existing blind super-resolution (SR) methods are generally time-consuming and less effective, as the estimation of degradation proceeds from a blind initialization and lacks interpretable degradation priors.
Learning to Track with Object Permanence
In this work, we introduce an end-to-end trainable approach for joint object detection and tracking that is capable of such reasoning.
Approximate Optimal Filter for Linear Gaussian Time-invariant Systems
The equivalence holds given certain conditions about initial state distributions and policy formats, in which the system state is the estimation error, control input is the filter gain, and control objective function is the accumulated estimation error.
Mixed Policy Gradient
MPG contains two types of PG: 1) data-driven PG, which is obtained by directly calculating the derivative of the learned Q-value function with respect to actions, and 2) model-driven PG, which is calculated using BPTT based on the model-predictive return.
Real-time tracking of COVID-19 and coronavirus research updates through text mining
The novel coronavirus (SARS-CoV-2) which causes COVID-19 is an ongoing pandemic.
Enhance Information Propagation for Graph Neural Network by Heterogeneous Aggregations
Graph neural networks are emerging as continuation of deep learning success w. r. t.
DPointNet: A Density-Oriented PointNet for 3D Object Detection in Point Clouds
In this paper, we put forward a novel density-oriented PointNet (DPointNet) for 3D object detection in point clouds, in which the density of points increases layer by layer.
Long time-series NDVI reconstruction in cloud-prone regions via spatio-temporal tensor completion
The applications of Normalized Difference Vegetation Index (NDVI) time-series data are inevitably hampered by cloud-induced gaps and noise.
Heterogeneous Graph based Deep Learning for Biomedical Network Link Prediction
Multi-scale biomedical knowledge networks are expanding with emerging experimental technologies that generates multi-scale biomedical big data.
Curvature-based Feature Selection with Application in Classifying Electronic Health Records
Disruptive technologies provides unparalleled opportunities to contribute to the identifications of many aspects in pervasive healthcare, from the adoption of the Internet of Things through to Machine Learning (ML) techniques.
Aha! Adaptive History-Driven Attack for Decision-Based Black-Box Models
A common practice is to start from a large perturbation and then iteratively reduce it with a deterministic direction and a random one while keeping it adversarial.
Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving
Second, we propose to learn a metric that combines the Mahalanobis and feature distances when comparing a track and a new detection in data association.
Two-Dimensional Multifunctional Materials from Endohedral Fullerenes
A new multifunctional 2D material is theoretically predicted based on systematic ab-initio calculations and model simulations for the honeycomb lattice of endohedral fullerene W@C28 molecules.
Materials Science Computational Physics
Skeleton-based Approaches based on Machine Vision: A Survey
Recently, skeleton-based approaches have achieved rapid progress on the basis of great success in skeleton representation.
Coherent mechanical noise cancellation and cooperativity competition in optomechanical arrays
Studying the interplay between multiple coupled mechanical resonators is a promising new direction in the field of optomechanics.
Optics Mesoscale and Nanoscale Physics
6 GHz hyperfast rotation of an optically levitated nanoparticle in vacuum
We measure a highest rotation frequency about 4. 3 GHz of the trapped nanoparticle without feedback cooling and a 6 GHz rotation with feedback cooling, which is the fastest mechanical rotation ever reported to date.
Optics Mesoscale and Nanoscale Physics Quantum Physics
Anatomy of Multipath BGP Deployment in a Large ISP Network
Our work provides insights into the latest deployment of M-BGP in a major ISP network and it highlights the characteristics and effectiveness of M-BGP as a means to realize load sharing.
Networking and Internet Architecture
Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion
In practice, an initial semantic segmentation (SS) of a single sweep point cloud can be achieved by any appealing network and then flows into the semantic scene completion (SSC) module as the input.
Ranked #2 on
3D Semantic Scene Completion
on SemanticKITTI
3D Semantic Scene Completion from a single RGB image
3D Semantic Segmentation
+2
Association: Remind Your GAN not to Forget
Neural networks are susceptible to catastrophic forgetting.
Spectral Response Function Guided Deep Optimization-driven Network for Spectral Super-resolution
Hyperspectral images are crucial for many research works.
Generative and Discriminative Learning for Distorted Image Restoration
Due to the uncertainty in the distortion variation, restoring distorted images caused by liquify filter is a challenging task.
The Occurrence of Rocky Habitable Zone Planets Around Solar-Like Stars from Kepler Data
We present occurrence rates for rocky planets in the habitable zones (HZ) of main-sequence dwarf stars based on the Kepler DR25 planet candidate catalog and Gaia-based stellar properties.
Earth and Planetary Astrophysics Solar and Stellar Astrophysics
Interpretable Detail-Fidelity Attention Network for Single Image Super-Resolution
To solve them, we propose a purposeful and interpretable detail-fidelity attention network to progressively process these smoothes and details in divide-and-conquer manner, which is a novel and specific prospect of image super-resolution for the purpose on improving the detail fidelity, instead of blindly designing or employing the deep CNNs architectures for merely feature representation in local receptive fields.
A Framework of Randomized Selection Based Certified Defenses Against Data Poisoning Attacks
Random selection based defenses can achieve certified robustness by averaging the classifiers' predictions on the sub-datasets sampled from the training set.
Extending Label Smoothing Regularization with Self-Knowledge Distillation
The experiment results confirm that the TC can help LsrKD and MrKD to boost training, especially on the networks they are failed.
A Density-Aware PointRCNN for 3D Object Detection in Point Clouds
We present an improved version of PointRCNN for 3D object detection, in which a multi-branch backbone network is adopted to handle the non-uniform density of point clouds.
A Light-Weight Object Detection Framework with FPA Module for Optical Remote Sensing Imagery
On the DOTA dataset, CenterFPANet mAP is 64. 00%, and FPS is 22. 2, which is close to the accuracy of the anchor-based methods currently used and much faster than them.
Align Deep Features for Oriented Object Detection
However most of existing methods rely on heuristically defined anchors with different scales, angles and aspect ratios and usually suffer from severe misalignment between anchor boxes and axis-aligned convolutional features, which leads to the common inconsistency between the classification score and localization accuracy.
Ranked #15 on
Object Detection In Aerial Images
on DOTA
PillarFlow: End-to-end Birds-eye-view Flow Estimation for Autonomous Driving
In autonomous driving, accurately estimating the state of surrounding obstacles is critical for safe and robust path planning.
Giant magnetic anisotropy energy and long coherence time of uranium substitution on defected Al2O3(0001)
Nanomagnets with giant magnetic anisotropy energy and long coherence time are desired for various technological innovations such as quantum information procession and storage.
Materials Science Computational Physics
Ternary Policy Iteration Algorithm for Nonlinear Robust Control
The simulation results show that the TPI algorithm can converge to the optimal solution for the linear plant, and has high resistance to disturbances for the nonlinear plant.
Balanced Symmetric Cross Entropy for Large Scale Imbalanced and Noisy Data
Deep convolution neural network has attracted many attentions in large-scale visual classification task, and achieves significant performance improvement compared to traditional visual analysis methods.
Weakly Supervised Temporal Action Localization with Segment-Level Labels
Specifically, we devise a partial segment loss regarded as a loss sampling to learn integral action parts from labeled segments.
Weakly-supervised Temporal Action Localization
Weakly Supervised Temporal Action Localization
Collaborative Boundary-aware Context Encoding Networks for Error Map Prediction
Further, we propose a context encoding module to utilize the global predictor from the error map to enhance the feature representation and regularize the networks.
Multi-Margin based Decorrelation Learning for Heterogeneous Face Recognition
Heterogeneous face recognition (HFR) refers to matching face images acquired from different domains with wide applications in security scenarios.
Projection & Probability-Driven Black-Box Attack
For reducing the solution space, we first model the adversarial perturbation optimization problem as a process of recovering frequency-sparse perturbations with compressed sensing, under the setting that random noise in the low-frequency space is more likely to be adversarial.
Anisotropic Convolutional Networks for 3D Semantic Scene Completion
In contrast to the standard 3D convolution that is limited to a fixed 3D receptive field, our module is capable of modeling the dimensional anisotropy voxel-wisely.
A Big Data Enabled Channel Model for 5G Wireless Communication Systems
This paper investigates various applications of big data analytics, especially machine learning algorithms in wireless communications and channel modeling.
Semantically-Guided Representation Learning for Self-Supervised Monocular Depth
Instead of using semantic labels and proxy losses in a multi-task approach, we propose a new architecture leveraging fixed pretrained semantic segmentation networks to guide self-supervised representation learning via pixel-adaptive convolutions.
3D Gated Recurrent Fusion for Semantic Scene Completion
This paper tackles the problem of data fusion in the semantic scene completion (SSC) task, which can simultaneously deal with semantic labeling and scene completion.
Ranked #14 on
3D Semantic Scene Completion
on NYUv2
Facial Attribute Capsules for Noise Face Super Resolution
In the SR processing, we first generated a group of FACs from the input LR face, and then reconstructed the HR face from this group of FACs.
Video Face Super-Resolution with Motion-Adaptive Feedback Cell
Current state-of-the-art CNN methods usually treat the VSR problem as a large number of separate multi-frame super-resolution tasks, at which a batch of low resolution (LR) frames is utilized to generate a single high resolution (HR) frame, and running a slide window to select LR frames over the entire video would obtain a series of HR frames.
Image Fine-grained Inpainting
Besides, we devise a geometrical alignment constraint item to compensate for the pixel-based distance between prediction features and ground-truth ones.
Ranked #1 on
Facial Inpainting
on FFHQ
Depth Based Semantic Scene Completion with Position Importance Aware Loss
We propose PALNet, a novel hybrid network for SSC based on single depth.
Probabilistic 3D Multi-Object Tracking for Autonomous Driving
Our method estimates the object states by adopting a Kalman Filter.
Cloud Removal with Fusion of High Resolution Optical and SAR Images Using Generative Adversarial Networks
The approach can be roughly divided into two steps: in the first step, a specially designed convolutional neural network (CNN) translates the synthetic aperture radar (SAR) images into simulated optical images in an object-to-object manner; in the second step, the simulated optical image, together with the SAR image and the optical image corrupted by clouds, is fused to reconstruct the corrupted area by a generative adversarial network (GAN) with a particular loss function.
Ranked #5 on
Cloud Removal
on SEN12MS-CR
Direct and indirect reinforcement learning
Reinforcement learning (RL) algorithms have been successfully applied to a range of challenging sequential decision making and control tasks.
Real-Time Panoptic Segmentation from Dense Detections
Panoptic segmentation is a complex full scene parsing task requiring simultaneous instance and semantic segmentation at high resolution.
HighEr-Resolution Network for Image Demosaicing and Enhancing
However, plenty of studies have shown that global information is crucial for image restoration tasks like image demosaicing and enhancing.
AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results
For training, only one set of source input images is therefore provided in the challenge.
Trident Segmentation CNN: A Spatiotemporal Transformation CNN for Punctate White Matter Lesions Segmentation in Preterm Neonates
In this paper, a novel spatiotemporal transformation deep learning method called Trident Segmentation CNN (TS-CNN) is proposed to segment PWML in MR images.
Two Stream Networks for Self-Supervised Ego-Motion Estimation
Learning depth and camera ego-motion from raw unlabeled RGB video streams is seeing exciting progress through self-supervision from strong geometric cues.
Robust Semi-Supervised Monocular Depth Estimation with Reprojected Distances
Dense depth estimation from a single image is a key problem in computer vision, with exciting applications in a multitude of robotic tasks.
ViLiVO: Virtual LiDAR-Visual Odometry for an Autonomous Vehicle with a Multi-Camera System
As for the pose tracker, we propose a visual odometry system fusing both the feature matching and the virtual LiDAR scan matching results.
Boosting Real-Time Driving Scene Parsing with Shared Semantics
On the other hand, feature fusion modules are designed to combine different modal of semantic features, which leverage the information from both inputs for better accuracy.
The Design and Implementation of a Real Time Visual Search System on JD E-commerce Platform
We present the design and implementation of a visual search system for real time image retrieval on JD. com, the world's third largest and China's largest e-commerce site.
PosNeg-Balanced Anchors with Aligned Features for Single-Shot Object Detection
We introduce a novel single-shot object detector to ease the imbalance of foreground-background class by suppressing the easy negatives while increasing the positives.
Progressive Perception-Oriented Network for Single Image Super-Resolution
In this paper, we propose a novel perceptual image super-resolution method that progressively generates visually high-quality results by constructing a stage-wise network.
Ranked #6 on
Image Super-Resolution
on Manga109 - 4x upscaling
Reconstructing Perceived Images from Brain Activity by Visually-guided Cognitive Representation and Adversarial Learning
In addition, we introduce a novel three-stage learning approach which enables the (cognitive) encoder to gradually distill useful knowledge from the paired (visual) encoder during the learning process.
Refined-Segmentation R-CNN: A Two-stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants
In this paper, we construct an efficient two-stage PWML semantic segmentation network based on the characteristics of the lesion, called refined segmentation R-CNN (RS RCNN).
A sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation
3D image segmentation is one of the most important and ubiquitous problems in medical image processing.
An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms
We train the deep encoder-decoder for landmark detection, and combine global landmark configuration with local high-resolution feature responses.
Enhancement of Power Equipment Management Using Knowledge Graph
Because of data duplication, database decentralization, weak data relations, and sluggish data updates, the power asset management system eager to adopt a new strategy to avoid the information losses, bias, and improve the data storage efficiency and extraction process.
No-Reference Quality Assessment of Contrast-Distorted Images using Contrast Enhancement
No-reference image quality assessment (NR-IQA) aims to measure the image quality without reference image.
The Speechtransformer for Large-scale Mandarin Chinese Speech Recognition
Attention-based sequence-to-sequence architectures have made great progress in the speech recognition task.
Shared Predictive Cross-Modal Deep Quantization
In this paper, we propose a deep quantization approach, which is among the early attempts of leveraging deep neural networks into quantization-based cross-modal similarity search.
Short-term Load Forecasting at Different Aggregation Levels with Predictability Analysis
Short-term load forecasting (STLF) is essential for the reliable and economic operation of power systems.
RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion
RGB images differentiate from depth images as they carry more details about the color and texture information, which can be utilized as a vital complementary to depth for boosting the performance of 3D semantic scene completion (SSC).
Ranked #18 on
3D Semantic Scene Completion
on NYUv2
Energy-Efficient Thermal Comfort Control in Smart Buildings via Deep Reinforcement Learning
We formulate the building thermal control as a cost-minimization problem which jointly considers the energy consumption of HVAC and the thermal comfort of the occupants.
Learning to Fuse Things and Stuff
We propose an end-to-end learning approach for panoptic segmentation, a novel task unifying instance (things) and semantic (stuff) segmentation.
Ranked #24 on
Panoptic Segmentation
on Cityscapes val
(using extra training data)
Universal Perturbation Attack Against Image Retrieval
In this paper, we make the first attempt in attacking image retrieval systems.
Improving Gated Recurrent Unit Based Acoustic Modeling with Batch Normalization and Enlarged Context
The use of future contextual information is typically shown to be helpful for acoustic modeling.
TESS full orbital phase curve of the WASP-18b system
The phase curve includes the transit, secondary eclipse, and sinusoidal modulations across the orbital phase shaped by the planet's atmospheric characteristics and the star-planet gravitational interaction.
Earth and Planetary Astrophysics Solar and Stellar Astrophysics
Tattoo Image Search at Scale: Joint Detection and Compact Representation Learning
To close the gap, we propose an efficient tattoo search approach that is able to learn tattoo detection and compact representation jointly in a single convolutional neural network (CNN) via multi-task learning.
SPIGAN: Privileged Adversarial Learning from Simulation
Deep Learning for Computer Vision depends mainly on the source of supervision. Photo-realistic simulators can generate large-scale automatically labeled syntheticdata, but introduce a domain gap negatively impacting performance.
Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient Network
The existence of hybrid noise in hyperspectral images (HSIs) severely degrades the data quality, reduces the interpretation accuracy of HSIs, and restricts the subsequent HSIs applications.
Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution
To capture more informative features and maintain long-term information for image super-resolution, we propose a channel-wise and spatial feature modulation (CSFM) network in which a sequence of feature-modulation memory (FMM) modules is cascaded with a densely connected structure to transform low-resolution features to high informative features.
Spatial-Spectral Fusion by Combining Deep Learning and Variation Model
In the field of spatial-spectral fusion, the model-based method and the deep learning (DL)-based method are state-of-the-art.
Bottom-up Pose Estimation of Multiple Person with Bounding Box Constraint
In this work, we propose a new method for multi-person pose estimation which combines the traditional bottom-up and the top-down methods.
Aspect Sentiment Classification with both Word-level and Clause-level AttentionNetworks
Aspect sentiment classification, a challenging taskin sentiment analysis, has been attracting more andmore attention in recent years.
Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network
Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to improve the performance of the subsequent HSI interpretation and applications.
Gated Recurrent Unit Based Acoustic Modeling with Future Context
The use of future contextual information is typically shown to be helpful for acoustic modeling.
AF-DCGAN: Amplitude Feature Deep Convolutional GAN for Fingerprint Construction in Indoor Localization System
Based on the extended fingerprint database, the accuracy of indoor localization system can be improved with reduced human effort.
Networking and Internet Architecture
Single Image Super-Resolution via Cascaded Multi-Scale Cross Network
To improve information flow and to capture sufficient knowledge for reconstructing the high-frequency details, we propose a cascaded multi-scale cross network (CMSC) in which a sequence of subnetworks is cascaded to infer high resolution features in a coarse-to-fine manner.
Restricting Greed in Training of Generative Adversarial Network
Generative adversarial network (GAN) has gotten wide re-search interest in the field of deep learning.
Planetary Candidates Observed by Kepler. VIII. A Fully Automated Catalog With Measured Completeness and Reliability Based on Data Release 25
For orbital periods less than 100 days the Robovetter completeness (the fraction of simulated transits that are determined to be planet candidates) across all observed stars is greater than 85%.
Earth and Planetary Astrophysics
Learning a Dilated Residual Network for SAR Image Despeckling
In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR) image despeckling, we propose a novel deep learning approach by learning a non-linear end-to-end mapping between the noisy and clean SAR images with a dilated residual network (SAR-DRN).
A survey of dimensionality reduction techniques based on random projection
Dimensionality reduction techniques play important roles in the analysis of big data.
WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images
Due to the depth-dependent water column effects inherent to underwater environments, we show that our end-to-end network implicitly learns a coarse depth estimate of the underwater scene from monocular underwater images.
Random Sampling for Fast Face Sketch Synthesis
The most time-consuming or main computation complexity for exemplar-based face sketch synthesis methods lies in the neighbor selection process.
Comparison among dimensionality reduction techniques based on Random Projection for cancer classification
FS followed by RP outperforms other combination methods in classification accuracy on most of the datasets.
Sparse Graphical Representation based Discriminant Analysis for Heterogeneous Face Recognition
An adaptive sparse graphical representation scheme is designed to represent heterogeneous face images, where a Markov networks model is constructed to generate adaptive sparse vectors.
Training-Free Synthesized Face Sketch Recognition Using Image Quality Assessment Metrics
In this paper, we proposed a synthesized face sketch recognition framework based on full-reference image quality assessment metrics.
Graphical Representation for Heterogeneous Face Recognition
Heterogeneous face recognition (HFR) refers to matching face images acquired from different sources (i. e., different sensors or different wavelengths) for identification.
