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Found 52 papers, 13 papers with code
Zoom Text Detector
However, the feature defocusing phenomenon that coarse layers are optimized by fine-grained objectives limits the extraction of semantic features.
Text Growing on Leaf
Then, lateral and thin veins are generated along the main vein growth direction in polar coordinates.
A Flow Base Bi-path Network for Cross-scene Video Crowd Understanding in Aerial View
Drones shooting can be applied in dynamic traffic monitoring, object detecting and tracking, and other vision tasks.
Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables
Neural processes (NPs) constitute a family of variational approximate models for stochastic processes with promising properties in computational efficiency and uncertainty quantification.
iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry
Objective: This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or digital-twins (DT) using patient medical images.
Pixel-wise Crowd Understanding via Synthetic Data
To be specific, 1) supervised crowd understanding: pre-train a crowd analysis model on the synthetic data, then fine-tune it using the real data and labels, which makes the model perform better on the real world; 2) crowd understanding via domain adaptation: translate the synthetic data to photo-realistic images, then train the model on translated data and labels.
Fusing Motion Patterns and Key Visual Information for Semantic Event Recognition in Basketball Videos
Based on the observations, we propose a scheme to fuse global and local motion patterns (MPs) and key visual information (KVI) for semantic event recognition in basketball videos.
Stable and Efficient Policy Evaluation
Policy evaluation algorithms are essential to reinforcement learning due to their ability to predict the performance of a policy.
Video Frame Interpolation via Residue Refinement
Video frame interpolation achieves temporal super-resolution by generating smooth transitions between frames.
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Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm
The reference algorithm, Automatminer, is a highly-extensible, fully-automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning.
Materials Science Computational Physics
Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting
Specifically, for a specific neuron of a source model, NLT exploits few labeled target data to learn domain shift parameters.
CNN-based Density Estimation and Crowd Counting: A Survey
Through our analysis, we expect to make reasonable inference and prediction for the future development of crowd counting, and meanwhile, it can also provide feasible solutions for the problem of object counting in other fields.
Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA
Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks.
Pixel-Level Self-Paced Learning for Super-Resolution
Recently, lots of deep networks are proposed to improve the quality of predicted super-resolution (SR) images, due to its widespread use in several image-based fields.
Focus on Semantic Consistency for Cross-domain Crowd Understanding
According to the semantic consistency, a similar distribution in deep layer's features of the synthetic and real-world crowd area, we first introduce a semantic extractor to effectively distinguish crowd and background in high-level semantic information.
A critical examination of compound stability predictions from machine-learned formation energies
By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85, 014 unique chemical compositions, we show that while formation energies can indeed be predicted well, all compositional models perform poorly on predicting the stability of compounds, making them considerably less useful than DFT for the discovery and design of new solids.
Materials Science Computational Physics
Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution
Deep learning-based hyperspectral image super-resolution (SR) methods have achieved great success recently.
NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization
In the last decade, crowd counting and localization attract much attention of researchers due to its wide-spread applications, including crowd monitoring, public safety, space design, etc.
Domain-adaptive Crowd Counting via High-quality Image Translation and Density Reconstruction
Recently, crowd counting using supervised learning achieves a remarkable improvement.
Weakly-Supervised Video Moment Retrieval via Semantic Completion Network
Video moment retrieval is to search the moment that is most relevant to the given natural language query.
Detecting AI Trojans Using Meta Neural Analysis
To train the meta-model without knowledge of the attack strategy, we introduce a technique called jumbo learning that samples a set of Trojaned models following a general distribution.
PolSAR Image Classification Based on Dilated Convolution and Pixel-Refining Parallel Mapping network in the Complex Domain
For general supervised deep learning classification algorithms, the pixel-by-pixel algorithm achieves precise yet inefficient classification with a small number of labeled pixels, whereas the pixel mapping algorithm achieves efficient yet edge-rough classification with more prior labels required.
SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting
The latter attempts to extract more discriminative features among different channels, which aids model to pay attention to the head region, the core of crowd scenes.
PCC Net: Perspective Crowd Counting via Spatial Convolutional Network
Crowd counting from a single image is a challenging task due to high appearance similarity, perspective changes and severe congestion.
Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification
In this paper, a novel locality and structure regularized low rank representation (LSLRR) model is proposed for HSI classification.
Embedding Structured Contour and Location Prior in Siamesed Fully Convolutional Networks for Road Detection
Road detection from the perspective of moving vehicles is a challenging issue in autonomous driving.
Learning by Inertia: Self-supervised Monocular Visual Odometry for Road Vehicles
In this paper, we present iDVO (inertia-embedded deep visual odometry), a self-supervised learning based monocular visual odometry (VO) for road vehicles.
GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection
In order to better handle high dimension problem and explore abundance information, this paper presents a General End-to-end Two-dimensional CNN (GETNET) framework for hyperspectral image change detection (HSI-CD).
VSSA-NET: Vertical Spatial Sequence Attention Network for Traffic Sign Detection
Our contributions are as follows: 1) We propose a multi-resolution feature fusion network architecture which exploits densely connected deconvolution layers with skip connections, and can learn more effective features for the small size object; 2) We frame the traffic sign detection as a spatial sequence classification and regression task, and propose a vertical spatial sequence attention (VSSA) module to gain more context information for better detection performance.
A Joint Convolutional Neural Networks and Context Transfer for Street Scenes Labeling
Our contributions are threefold: (1) A priori s-CNNs model that learns priori location information at superpixel level is proposed to describe various objects discriminatingly; (2) A hierarchical data augmentation method is presented to alleviate dataset bias in the priori s-CNNs training stage, which improves foreground objects labeling significantly; (3) A soft restricted MRF energy function is defined to improve the priori s-CNNs model's labeling performance and reduce the over smoothness at the same time.
Memory-Augmented Temporal Dynamic Learning for Action Recognition
Human actions captured in video sequences contain two crucial factors for action recognition, i. e., visual appearance and motion dynamics.
Early Action Prediction with Generative Adversarial Networks
Action Prediction is aimed to determine what action is occurring in a video as early as possible, which is crucial to many online applications, such as predicting a traffic accident before it happens and detecting malicious actions in the monitoring system.
Anomaly Detection in Traffic Scenes via Spatial-aware Motion Reconstruction
3) Results of motion orientation and magnitude are adaptively weighted and fused by a Bayesian model, which makes the proposed method more robust and handle more kinds of abnormal events.
Cross-Modal Message Passing for Two-stream Fusion
The classification object ensures that each modal network predicts the true action category while the competing objective encourages each modal network to outperform the other one.
Optimal Clustering Framework for Hyperspectral Band Selection
Band selection, by choosing a set of representative bands in hyperspectral image (HSI), is an effective method to reduce the redundant information without compromising the original contents.
Forward Vehicle Collision Warning Based on Quick Camera Calibration
Forward Vehicle Collision Warning (FCW) is one of the most important functions for autonomous vehicles.
Tracking as A Whole: Multi-Target Tracking by Modeling Group Behavior with Sequential Detection
In the detection stage, we present a sequential detection model to deal with serious occlusions.
Weakly Supervised Adversarial Domain Adaptation for Semantic Segmentation in Urban Scenes
In this paper, we propose a weakly supervised adversarial domain adaptation to improve the segmentation performance from synthetic data to real scenes, which consists of three deep neural networks.
Listen to the Image
different encoding schemes indicate that using machine model to accelerate optimization evaluation and reduce experimental cost is feasible to some extent, which could dramatically promote the upgrading of encoding scheme then help the blind to improve their visual perception ability.
Linking plastic heterogeneity of bulk metallic glasses to quench-in structural defects with machine learning
When metallic glasses are subjected to mechanical loads, the plastic response of atoms is heterogeneous.
Materials Science Computational Physics
Learning from Synthetic Data for Crowd Counting in the Wild
Secondly, we propose two schemes that exploit the synthetic data to boost the performance of crowd counting in the wild: 1) pretrain a crowd counter on the synthetic data, then finetune it using the real data, which significantly prompts the model's performance on real data; 2) propose a crowd counting method via domain adaptation, which can free humans from heavy data annotations.
Deep Bayesian Multi-Target Learning for Recommender Systems
This work introduces a multi-target optimization framework with Bayesian modeling of the target events, called Deep Bayesian Multi-Target Learning (DBMTL).
PatientEG Dataset: Bringing Event Graph Model with Temporal Relations to Electronic Medical Records
Based on the proposed model, we also construct a PatientEG dataset with 191, 294 events, 3, 429 distinct entities, and 545, 993 temporal relations using EMRs from Shanghai Shuguang hospital.
VMAV-C: A Deep Attention-based Reinforcement Learning Algorithm for Model-based Control
Recent breakthroughs in Go play and strategic games have witnessed the great potential of reinforcement learning in intelligently scheduling in uncertain environment, but some bottlenecks are also encountered when we generalize this paradigm to universal complex tasks.
SDFN: Segmentation-based Deep Fusion Network for Thoracic Disease Classification in Chest X-ray Images
Two CNN-based classification models were then used as feature extractors to obtain the discriminative features of the entire CXR images and the cropped lung region images.
DT-LET: Deep Transfer Learning by Exploring where to Transfer
Previous transfer learning methods based on deep network assume the knowledge should be transferred between the same hidden layers of the source domain and the target domains.
Fast and Accurate Recognition of Chinese Clinical Named Entities with Residual Dilated Convolutions
In this paper, we propose a Residual Dilated Convolutional Neural Network with Conditional Random Field (RD-CNN-CRF) to solve it.
Automatic Severity Classification of Coronary Artery Disease via Recurrent Capsule Network
Coronary artery disease (CAD) is one of the leading causes of cardiovascular disease deaths.
An attention-based Bi-GRU-CapsNet model for hypernymy detection between compound entities
In this paper, we present an attention-based Bi-GRU-CapsNet model to detect hypernymy relationship between compound entities.
Incorporating Dictionaries into Deep Neural Networks for the Chinese Clinical Named Entity Recognition
Clinical Named Entity Recognition (CNER) aims to identify and classify clinical terms such as diseases, symptoms, treatments, exams, and body parts in electronic health records, which is a fundamental and crucial task for clinical and translational research.
From Maxout to Channel-Out: Encoding Information on Sparse Pathways
Motivated by an important insight from neural science, we propose a new framework for understanding the success of the recently proposed "maxout" networks.
