Search Results for author:
Found 125 papers, 57 papers with code
RGB-D Saliency Detection via Cascaded Mutual Information Minimization
In this paper, we introduce a novel multi-stage cascaded learning framework via mutual information minimization to "explicitly" model the multi-modal information between RGB image and depth data.
Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images
In this paper, we propose a novel stagewise domain adaptation model called RoadDA to address the DS issue in this field.
AP-10K: A Benchmark for Animal Pose Estimation in the Wild
The experimental results provide sound empirical evidence on the superiority of learning from diverse animals species in terms of both accuracy and generalization ability.
Out-of-boundary View Synthesis Towards Full-Frame Video Stabilization
However, since the view outside the boundary is not available during warping, the resulting holes around the boundary of the stabilized frame must be discarded (i. e., cropping) to maintain visual consistency, and thus does leads to a tradeoff between stability and cropping ratio.
GCCAD: Graph Contrastive Coding for Anomaly Detection
To achieve the contrastive objective, we design a graph neural network encoder that can infer and further remove suspicious links during message passing, as well as learn the global context of the input graph.
Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images
Recent studies indicate that the SCD can be modeled through a triple-branch Convolutional Neural Network (CNN), which contains two temporal branches and a change branch.
Learning Visual Affordance Grounding from Demonstration Videos
For the object branch, we introduce a semantic enhancement module (SEM) to make the network focus on different parts of the object according to the action classes and utilize a distillation loss to align the output features of the object branch with that of the video branch and transfer the knowledge in the video branch to the object branch.
One-Shot Object Affordance Detection in the Wild
To empower robots with this ability in unseen scenarios, we first study the challenging one-shot affordance detection problem in this paper, i. e., given a support image that depicts the action purpose, all objects in a scene with the common affordance should be detected.
I3CL:Intra- and Inter-Instance Collaborative Learning for Arbitrary-shaped Scene Text Detection
Existing methods for arbitrary-shaped text detection in natural scenes face two critical issues, i. e., 1) fracture detections at the gaps in a text instance; and 2) inaccurate detections of arbitrary-shaped text instances with diverse background context.
TA-MAMC at SemEval-2021 Task 4: Task-adaptive Pretraining and Multi-head Attention for Abstract Meaning Reading Comprehension
This paper describes our system used in the SemEval-2021 Task4 Reading Comprehension of Abstract Meaning, achieving 1st for subtask 1 and 2nd for subtask 2 on the leaderboard.
Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers
In DQFA, a novel domain query is used to aggregate and align global context from the token sequence of both domains.
DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation
In addition, feature-level alignment is carried out by aligning the feature maps of the source and target images from student network using a weighted maximum mean discrepancy loss.
Deep Automatic Natural Image Matting
To address the problem, a novel end-to-end matting network is proposed, which can predict a generalized trimap for any image of the above types as a unified semantic representation.
Ranked #1 on
Image Matting
on AIM-500
Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images
To break this limitation, we propose a Wide-Context Network (WiCoNet) for the semantic segmentation of HR RSIs.
One-Shot Affordance Detection
To empower robots with this ability in unseen scenarios, we consider the challenging one-shot affordance detection problem in this paper, i. e., given a support image that depicts the action purpose, all objects in a scene with the common affordance should be detected.
Few-Shot Domain Expansion for Face Anti-Spoofing
In practice, given a handful of labeled samples from a new deployment scenario (target domain) and abundant labeled face images in the existing source domain, the FAS system is expected to perform well in the new scenario without sacrificing the performance on the original domain.
Energy-Based Generative Cooperative Saliency Prediction
Specifically, we propose a generative cooperative saliency prediction framework based on the generative cooperative networks, where a conditional latent variable model and a conditional energy-based model are jointly trained to predict saliency in a cooperative manner.
Depth-Guided Camouflaged Object Detection
Camouflaged object detection (COD) aims to segment camouflaged objects hiding in the environment, which is challenging due to the similar appearance of camouflaged objects and their surroundings.
Confidence-Aware Learning for Camouflaged Object Detection
Then, we concatenate it with the input image and feed it to the confidence estimation network to produce an one channel confidence map. We generate dynamic supervision for the confidence estimation network, representing the agreement of camouflage prediction with the ground truth camouflage map.
Invertible Attention
Attention has been proved to be an efficient mechanism to capture long-range dependencies.
A Comprehensive Survey on Image Dehazing Based on Deep Learning
The presence of haze significantly reduces the quality of images.
ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias
Nevertheless, vision transformers treat an image as 1D sequence of visual tokens, lacking an intrinsic inductive bias (IB) in modeling local visual structures and dealing with scale variance.
Ranked #1 on
Object Detection
on DAVIS-2016
SHD360: A Benchmark Dataset for Salient Human Detection in 360° Videos
However, 360{\deg} video SHD has been seldom discussed in the computer vision community due to a lack of datasets with large-scale omnidirectional videos and rich annotations.
Salient Objects in Clutter
This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets.
End-to-end One-shot Human Parsing
Previous human parsing models are limited to parsing humans into pre-defined classes, which is inflexible for applications that need to handle new classes.
Privacy-Preserving Portrait Matting
We systematically evaluate both trimap-free and trimap-based matting methods on P3M-10k and find that existing matting methods show different generalization capabilities when following the Privacy-Preserving Training (PPT) setting, i. e., training on face-blurred images and testing on arbitrary images.
Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction
Motivated by this observation, we propose a novel \emph{CVR} prediction method by Hierarchically Modeling both Micro and Macro behaviors ($HM^3$).
Transformer Transforms Salient Object Detection and Camouflaged Object Detection
Extensive experimental results on various SOD and COD tasks illustrate that transformer networks can transform SOD and COD, leading to new benchmarks for each related task.
Learning structure-aware semantic segmentation with image-level supervision
Within this pipeline, the class activation map (CAM) is obtained and further processed to serve as a pseudo label to train the semantic segmentation model in a fully-supervised manner.
Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data
Electronic Health Record (EHR) data has been of tremendous utility in Artificial Intelligence (AI) for healthcare such as predicting future clinical events.
Uncertainty-aware Joint Salient Object and Camouflaged Object Detection
Visual salient object detection (SOD) aims at finding the salient object(s) that attract human attention, while camouflaged object detection (COD) on the contrary intends to discover the camouflaged object(s) that hidden in the surrounding.
Weakly Supervised Video Salient Object Detection
Significant performance improvement has been achieved for fully-supervised video salient object detection with the pixel-wise labeled training datasets, which are time-consuming and expensive to obtain.
VDM-DA: Virtual Domain Modeling for Source Data-free Domain Adaptation
On the other hand, we also design an effective distribution alignment method to reduce the distribution divergence between the virtual domain and the target domain by gradually improving the compactness of the target domain distribution through model learning.
Forest Fire Clustering: Iterative Label Propagation Clustering and Monte Carlo Validation For Single-cell Sequencing Analysis
With the rise of single-cell sequencing technologies, there is a growing need for robust clustering algorithms to extract deeper insights from data.
Simultaneously Localize, Segment and Rank the Camouflaged Objects
With the above understanding about camouflaged objects, we present the first ranking based COD network (Rank-Net) to simultaneously localize, segment and rank camouflaged objects.
Understanding WeChat User Preferences and "Wow" Diffusion
"Top Stories" is a novel friend-enhanced recommendation engine in WeChat, in which users can read articles based on preferences of both their own and their friends.
Graph Representation Learning
Social and Information Networks
Koopmans' theorem as the mechanism of nearly gapless surface states in self-doped magnetic topological insulators
The magnetization-induced gap at the surface state is widely believed as the kernel of magnetic topological insulators (MTIs) because of its relevance to various topological phenomena, such as the quantum anomalous Hall effect and the axion insulator phase.
Materials Science
Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography
Deep learning based subtomogram classification have played critical roles for such tasks.
Multi-Stage Transmission Line Flow Control Using Centralized and Decentralized Reinforcement Learning Agents
At stage one, centralized soft actor-critic (SAC) agent is trained to control generator active power outputs in a wide area to control transmission line flows against specified security limits.
Information-Theoretic Odometry Learning
In this paper, we propose a unified information-theoretic framework for odometry learning, a crucial component of many robotics and vision tasks such as navigation and virtual reality where 6-DOF poses are required in real time.
TextTN: Probabilistic Encoding of Language on Tensor Network
As a novel model that bridges machine learning and quantum theory, tensor network (TN) has recently gained increasing attention and successful applications for processing natural images.
Recommending Courses in MOOCs for Jobs: An Auto Weak Supervision Approach
On the one hand, the framework enables training multiple supervised ranking models upon the pseudo labels produced by multiple unsupervised ranking models.
Memory-Gated Recurrent Networks
The essence of multivariate sequential learning is all about how to extract dependencies in data.
Residual Matrix Product State for Machine Learning
Tensor network (TN), which originates from quantum physics, shows broad prospects in classical and quantum machine learning (ML).
Progressive One-shot Human Parsing
In this paper, we devise a novel Progressive One-shot Parsing network (POPNet) to address two critical challenges , i. e., testing bias and small sizes.
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
SPOC learner's final grade prediction based on a novel sampling batch normalization embedded neural network method
To solve this problem, a sampling batch normalization embedded deep neural network (SBNEDNN) method is developed in this paper.
COAD: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking
Expert finding, a popular service provided by many online websites such as Expertise Finder, LinkedIn, and AMiner, benefits seeking consultants, collaborators, and candidate qualifications.
Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation
Cold-start problem is a fundamental challenge for recommendation tasks.
Uncertainty-Aware Deep Calibrated Salient Object Detection
Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy.
Meta Learning-based MIMO Detectors: Design, Simulation, and Experimental Test
An unfolded turbo decoding module, called TurboNet, is used for channel decoding.
Auto Learning Attention
Attention modules have been demonstrated effective in strengthening the representation ability of a neural network via reweighting spatial or channel features or stacking both operations sequentially.
3D Guided Weakly Supervised Semantic Segmentation
In this paper, we propose a weakly supervised 2D semantic segmentation model by incorporating sparse bounding box labels with available 3D information, which is much easier to obtain with advanced sensors.
2D Semantic Segmentation
Weakly-Supervised Semantic Segmentation
DUT: Learning Video Stabilization by Simply Watching Unstable Videos
We propose a Deep Unsupervised Trajectory-based stabilization framework (DUT) in this paper.
Inter-layer Transition in Neural Architecture Search
Differential Neural Architecture Search (NAS) methods represent the network architecture as a repetitive proxy directed acyclic graph (DAG) and optimize the network weights and architecture weights alternatively in a differential manner.
SIR: Self-supervised Image Rectification via Seeing the Same Scene from Multiple Different Lenses
However, the model may overfit the synthetic images and generalize not well on real-world fisheye images due to the limited universality of a specific distortion model and the lack of explicitly modeling the distortion and rectification process.
Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things
AI introduced into the IoT heralds the era of artificial intelligence of things (AIoT).
Bridge Composite and Real: Towards End-to-end Deep Image Matting
Furthermore, we provide a benchmark containing 2, 000 high-resolution real-world animal images and 10, 000 portrait images along with their manually labeled alpha mattes to serve as a test bed for evaluating matting model's generalization ability on real-world images.
Ranked #1 on
Image Matting
on AM-2K
Wide-angle Image Rectification: A Survey
In this paper, we comprehensively survey progress in wide-angle image rectification from transformation models to rectification methods.
Neural, Symbolic and Neural-Symbolic Reasoning on Knowledge Graphs
On the contrary, the recent advances of deep learning promote neural reasoning on knowledge graphs, which is robust to the ambiguous and noisy data, but lacks interpretability compared to symbolic reasoning.
Uncertainty Inspired RGB-D Saliency Detection
Our framework includes two main models: 1) a generator model, which maps the input image and latent variable to stochastic saliency prediction, and 2) an inference model, which gradually updates the latent variable by sampling it from the true or approximate posterior distribution.
Ranked #1 on
RGB Salient Object Detection
on ECSSD
RGB-D Salient Object Detection
RGB Salient Object Detection
+1
MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG
MLBF-Net is composed of three components: 1) multiple lead-specific branches for learning the diversity of multi-lead ECG; 2) cross-lead features fusion by concatenating the output feature maps of all branches for learning the integrity of multi-lead ECG; 3) multi-loss co-optimization for all the individual branches and the concatenated network.
Nighttime Dehazing with a Synthetic Benchmark
To address this issue, we propose a novel synthetic method called 3R to simulate nighttime hazy images from daytime clear images, which first reconstructs the scene geometry, then simulates the light rays and object reflectance, and finally renders the haze effects.
Generative networks as inverse problems with fractional wavelet scattering networks
In order to solve or alleviate the synchronous training difficult problems of GANs and VAEs, recently, researchers propose Generative Scattering Networks (GSNs), which use wavelet scattering networks (ScatNets) as the encoder to obtain the features (or ScatNet embeddings) and convolutional neural networks (CNNs) as the decoder to generate the image.
Learning Noise-Aware Encoder-Decoder from Noisy Labels by Alternating Back-Propagation for Saliency Detection
The proposed model consists of two sub-models parameterized by neural networks: (1) a saliency predictor that maps input images to clean saliency maps, and (2) a noise generator, which is a latent variable model that produces noises from Gaussian latent vectors.
Structured Massive Access for Scalable Cell-Free Massive MIMO Systems
However, there are still many unsolved practical issues in cell-free massive MIMO systems, whereof scalable massive access implementation is one of the most vital.
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
Graph representation learning has emerged as a powerful technique for addressing real-world problems.
Model-Driven DNN Decoder for Turbo Codes: Design, Simulation and Experimental Results
The TurboNet inherits the superiority of the max-log-MAP algorithm and DL tools and thus presents excellent error-correction capability with low training cost.
Self-supervised Learning: Generative or Contrastive
As an alternative, self-supervised learning attracts many researchers for its soaring performance on representation learning in the last several years.
Condensing Two-stage Detection with Automatic Object Key Part Discovery
Modern two-stage object detectors generally require excessively large models for their detection heads to achieve high accuracy.
Unsupervised Domain Expansion from Multiple Sources
Given an existing system learned from previous source domains, it is desirable to adapt the system to new domains without accessing and forgetting all the previous domains in some applications.
Gleason Score Prediction using Deep Learning in Tissue Microarray Image
Prostate cancer (PCa) is one of the most common cancers in men around the world.
Multi-Target Deep Learning for Algal Detection and Classification
Water quality has a direct impact on industry, agriculture, and public health.
LineaRE: Simple but Powerful Knowledge Graph Embedding for Link Prediction
Specifically, we regard knowledge graph embedding as a simple linear regression task, where a relation is modeled as a linear function of two low-dimensional vector-presented entities with two weight vectors and a bias vector.
UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
In this paper, we propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process.
Ranked #4 on
RGB-D Salient Object Detection
on LFSD
Active Learning Approach to Optimization of Experimental Control
In this work we present a general machine learning based scheme to optimize experimental control.
Weakly-Supervised Salient Object Detection via Scribble Annotations
In this paper, we propose a weakly-supervised salient object detection model to learn saliency from such annotations.
Registration of multi-view point sets under the perspective of expectation-maximization
Registration of multi-view point sets is a prerequisite for 3D model reconstruction.
Towards High Performance Human Keypoint Detection
Human keypoint detection from a single image is very challenging due to occlusion, blur, illumination and scale variance.
Ensemble emotion recognizing with multiple modal physiological signals
Multiple physiological signals fusing models, building the uniform classification model by means of consistent and complementary information from different emotions to improve recognition performance.
Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge
Given a text description, we immediately imagine an overall visual impression using this prior and, based on this, we draw a picture by progressively adding more and more details.
Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsing
In this paper, we propose a novel GRAph PYramid Mutual Learning (Grapy-ML) method to address the cross-dataset human parsing problem, where the annotations are at different granularities.
Progressive Retinex: Mutually Reinforced Illumination-Noise Perception Network for Low Light Image Enhancement
Contrast enhancement and noise removal are coupled problems for low-light image enhancement.
Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation
Although there has been a progress in matching the marginal distributions between two domains, the classifier favors the source domain features and makes incorrect predictions on the target domain due to category-agnostic feature alignment.
Ranked #15 on
Image-to-Image Translation
on SYNTHIA-to-Cityscapes
Human Keypoint Detection by Progressive Context Refinement
Human keypoint detection from a single image is very challenging due to occlusion, blur, illumination and scale variance of person instances.
Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction
Although existing methods, typically built on the user sequential behavior path ``impression$\to$click$\to$purchase'', is effective for dealing with SSB issue, they still struggle to address the DS issue due to rare purchase training samples.
MIOpen: An Open Source Library For Deep Learning Primitives
Deep Learning has established itself to be a common occurrence in the business lexicon.
Semi-Supervised Graph Embedding for Multi-Label Graph Node Classification
The dimension of the label vector is the same as that of the node vector before the last convolution operation of GCN.
FAMED-Net: A Fast and Accurate Multi-scale End-to-end Dehazing Network
Single image dehazing is a critical image pre-processing step for subsequent high-level computer vision tasks.
Computer-aided Detection of Squamous Carcinoma of the Cervix in Whole Slide Images
Traditionally, the most indispensable diagnosis of cervix squamous carcinoma is histopathological assessment which is achieved under microscope by pathologist.
Deep Learning Based on Orthogonal Approximate Message Passing for CP-Free OFDM
The DL-OAMP receiver includes a channel estimation neural network (CE-Net) and a signal detection neural network based on OAMP, called OAMP-Net.
Few-Shot Learning via Saliency-guided Hallucination of Samples
To the best of our knowledge, we are the first to leverage saliency maps for such a task and we demonstrate their usefulness in hallucinating additional datapoints for few-shot learning.
Progressive LiDAR Adaptation for Road Detection
To this end, LiDAR sensor data can be incorporated to improve the visual image-based road detection, because LiDAR data is less susceptible to visual noises.
MirrorGAN: Learning Text-to-image Generation by Redescription
Generating an image from a given text description has two goals: visual realism and semantic consistency.
Ranked #5 on
Text-to-Image Generation
on CUB
Artificial Intelligence-aided Receiver for A CP-Free OFDM System: Design, Simulation, and Experimental Test
The AI receiver includes a channel estimation neural network (CE-NET) and a signal detection neural network based on orthogonal approximate message passing (OAMP), called OAMP-NET.
Information Theory Information Theory
Attributes-aided Part Detection and Refinement for Person Re-identification
Person attributes are often exploited as mid-level human semantic information to help promote the performance of person re-identification task.
Anomaly detecting and ranking of the cloud computing platform by multi-view learning
We presented an online model for anomaly detecting using machine learning theory.
Deep Time-Frequency Representation and Progressive Decision Fusion for ECG Classification
Early recognition of abnormal rhythms in ECG signals is crucial for monitoring and diagnosing patients' cardiac conditions, increasing the success rate of the treatment.
Artificial Intelligence-aided OFDM Receiver: Design and Experimental Results
To address the performance gap between the simulation and the OTA test caused by the discrepancy between the channel model for offline training and real environments, we develop a novel online training strategy, called SwitchNet receiver.
Background Subtraction using Compressed Low-resolution Images
The essential first step involved in almost all the visual tasks is background subtraction with a static camera.
Marrying Tracking with ELM: A Metric Constraint Guided Multiple Feature Fusion Method
In this paper, we solve the problem from multi-view perspective by leveraging multi-view complementary and latent information, so as to be robust to the partial occlusion and background clutter especially when the objects are similar to the target, meanwhile addressing tracking drift.
Robust Tracking via Weighted Online Extreme Learning Machine
Therefore, our tracking method can fully learn both of the target object and background information to enhance the tracking performance, and it is evaluated in 20 challenge image sequences with different attributes including illumination, occlusion, deformation, etc., which achieves better performance than several state-of-the-art methods in terms of effectiveness and robustness.
Towards Practical Visual Search Engine within Elasticsearch
In this paper, we describe our end-to-end content-based image retrieval system built upon Elasticsearch, a well-known and popular textual search engine.
Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System
The deep cascade structure and the combination rule enable the proposed \textit{ldcTree} to have a stronger distributed feature representation ability.
Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective
Such supervision, while labor-intensive and not always possible, tends to hinder the generalization ability of the learned models.
Unsupervised Domain Adaptation: A Multi-task Learning-based Method
This paper presents a novel multi-task learning-based method for unsupervised domain adaptation.
Importance Weighted Adversarial Nets for Partial Domain Adaptation
This paper proposes an importance weighted adversarial nets-based method for unsupervised domain adaptation, specific for partial domain adaptation where the target domain has less number of classes compared to the source domain.
Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images
Specifically, we propose to randomly shuffle the pixels in the origin images and leverage the shuffled image as input to make CNN more concerned with the statistical properties.
Deep Edge-Aware Saliency Detection
There has been profound progress in visual saliency thanks to the deep learning architectures, however, there still exist three major challenges that hinder the detection performance for scenes with complex compositions, multiple salient objects, and salient objects of diverse scales.
Fast Haze Removal for Nighttime Image Using Maximum Reflectance Prior
Then, we propose a simple but effective image prior, maximum reflectance prior, to estimate the varying ambient illumination.
Integrated Deep and Shallow Networks for Salient Object Detection
Then the results from deep FCNN and RBD are concatenated to feed into a shallow network to map the concatenated feature maps to saliency maps.
Unfolding Hidden Barriers by Active Enhanced Sampling
We introduce an active learning scheme that consists of a parametric CV learner based on deep neural network and a CV-based enhanced sampler.
Joint Geometrical and Statistical Alignment for Visual Domain Adaptation
This paper presents a novel unsupervised domain adaptation method for cross-domain visual recognition.
Ranked #5 on
Domain Adaptation
on Office-Caltech
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition.
Data-Driven Estimation of Travel Latency Cost Functions via Inverse Optimization in Multi-Class Transportation Networks
We develop a method to estimate from data travel latency cost functions in multi-class transportation networks, which accommodate different types of vehicles with very different characteristics (e. g., cars and trucks).
Systems and Control Optimization and Control 90C33, 90C90, 90C30
Statistical Anomaly Detection via Composite Hypothesis Testing for Markov Models
Under Markovian assumptions, we leverage a Central Limit Theorem (CLT) for the empirical measure in the test statistic of the composite hypothesis Hoeffding test so as to establish weak convergence results for the test statistic, and, thereby, derive a new estimator for the threshold needed by the test.
Data-driven Estimation of Origin-Destination Demand and User Cost Functions for the Optimization of Transportation Networks
In earlier work (Zhang et al., 2016) we used actual traffic data from the Eastern Massachusetts transportation network in the form of spatial average speeds and road segment flow capacities in order to estimate Origin-Destination (OD) flow demand matrices for the network.
Systems and Control 90B06
Nighttime Haze Removal with Illumination Correction
ii) Then it achieves a color-balance result by performing a color correction step after estimating the color characteristics of the incident light.
RGB-D-based Action Recognition Datasets: A Survey
Human action recognition from RGB-D (Red, Green, Blue and Depth) data has attracted increasing attention since the first work reported in 2010.
Action recognition from depth maps using deep convolutional neural networks
In addition, the method was evaluated on the large dataset constructed from the above datasets.
Ranked #9 on
Multimodal Activity Recognition
on EV-Action
Panther: Fast Top-k Similarity Search in Large Networks
The algorithm is based on a novel idea of random path, and an extended method is also presented, to enhance the structural similarity when two vertices are completely disconnected.
Social and Information Networks
Deep Convolutional Neural Networks for Action Recognition Using Depth Map Sequences
The results show that our approach can achieve state-of-the-art results on the individual datasets and without dramatical performance degradation on the Combined Dataset.