Search Results for author:
Found 456 papers, 178 papers with code
Task-group Relatedness and Generalization Bounds for Regularized Multi-task Learning
We are mainly concerned with two theoretical questions: 1) under what conditions does RMTL perform better with a smaller task sample size than STL?
Statistical Properties of the Single Linkage Hierarchical Clustering Estimator
Distance-based hierarchical clustering (HC) methods are widely used in unsupervised data analysis but few authors take account of uncertainty in the distance data.
Maximum Likelihood Estimation for Single Linkage Hierarchical Clustering
We derive a statistical model for estimation of a dendrogram from single linkage hierarchical clustering (SLHC) that takes account of uncertainty through noise or corruption in the measurements of separation of data.
Partial Person Re-Identification
We address a new partial person re-identification (re-id) problem, where only a partial observation of a person is available for matching across different non-overlapping camera views.
Multi-Scale Learning for Low-Resolution Person Re-Identification
In real world person re-identification (re-id), images of people captured at very different resolutions from different locations need be matched.
StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation
In this paper, we propose StalemateBreaker, a conversation system that can proactively introduce new content when appropriate.
An Enhanced Deep Feature Representation for Person Re-identification
In this paper, we focus on the feature representation and claim that hand-crafted histogram features can be complementary to Convolutional Neural Network (CNN) features.
Top-push Video-based Person Re-identification
Since only limited information can be exploited from still images, it is hard (if not impossible) to overcome the occlusion, pose and camera-view change, and lighting variation problems.
Two are Better than One: An Ensemble of Retrieval- and Generation-Based Dialog Systems
In this paper, we propose a novel ensemble of retrieval-based and generation-based dialog systems in the open domain.
LightRNN: Memory and Computation-Efficient Recurrent Neural Networks
Based on the 2-Component shared embedding, we design a new RNN algorithm and evaluate it using the language modeling task on several benchmark datasets.
Supervised Community Detection with Line Graph Neural Networks
We show that, in a data-driven manner and without access to the underlying generative models, they can match or even surpass the performance of the belief propagation algorithm on binary and multi-class stochastic block models, which is believed to reach the computational threshold.
Ranked #1 on
Community Detection
on Amazon
(Accuracy-NE metric, using extra
training data)
Learning the Sparse and Low Rank PARAFAC Decomposition via the Elastic Net
In this article, we derive a Bayesian model to learning the sparse and low rank PARAFAC decomposition for the observed tensor with missing values via the elastic net, with property to find the true rank and sparse factor matrix which is robust to the noise.
Joint Intensity and Spatial Metric Learning for Robust Gait Recognition
This paper describes a joint intensity metric learning method to improve the robustness of gait recognition with silhouette-based descriptors such as gait energy images.
Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis
However, the development of a robust and reliable deep learning model for computer-aided diagnosis is still highly challenging due to the combination of the high heterogeneity in the medical images and the relative lack of training samples.
Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis
In order to address the limitations of the unsupervised DLSC-based fMRI studies, we utilize the prior knowledge of task paradigm in the learning step to train a data-driven dictionary and to model the sparse representation.
Developing Knowledge-enhanced Chronic Disease Risk Prediction Models from Regional EHR Repositories
Precision medicine requires the precision disease risk prediction models.
Improved Representation Learning for Predicting Commonsense Ontologies
Recent work in learning ontologies (hierarchical and partially-ordered structures) has leveraged the intrinsic geometry of spaces of learned representations to make predictions that automatically obey complex structural constraints.
On better training the infinite restricted Boltzmann machines
To break this dependency between neighboring hidden units and speed up the convergence of training, a novel training strategy is proposed.
Image Segmentation and Classification for Sickle Cell Disease using Deformable U-Net
Reliable cell segmentation and classification from biomedical images is a crucial step for both scientific research and clinical practice.
Medical Image Segmentation Based on Multi-Modal Convolutional Neural Network: Study on Image Fusion Schemes
Image analysis using more than one modality (i. e. multi-modal) has been increasingly applied in the field of biomedical imaging.
Unsupervised Multi-Domain Image Translation with Domain-Specific Encoders/Decoders
Unsupervised Image-to-Image Translation achieves spectacularly advanced developments nowadays.
Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal
Specifically, a shadow image is fed into the first generator which produces a shadow detection mask.
Ranked #3 on
RGB Salient Object Detection
on ISTD
Learning Tree-based Deep Model for Recommender Systems
In systems with large corpus, however, the calculation cost for the learnt model to predict all user-item preferences is tremendous, which makes full corpus retrieval extremely difficult.
Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift
Theoretically, we find that Dropout would shift the variance of a specific neural unit when we transfer the state of that network from train to test.
SESR: Single Image Super Resolution with Recursive Squeeze and Excitation Networks
Single image super resolution is a very important computer vision task, with a wide range of applications.
Ranked #34 on
Image Super-Resolution
on BSD100 - 4x upscaling
Densely Connected Bidirectional LSTM with Applications to Sentence Classification
Deep neural networks have recently been shown to achieve highly competitive performance in many computer vision tasks due to their abilities of exploring in a much larger hypothesis space.
Mixed Link Networks
Basing on the analysis by revealing the equivalence of modern networks, we find that both ResNet and DenseNet are essentially derived from the same "dense topology", yet they only differ in the form of connection -- addition (dubbed "inner link") vs. concatenation (dubbed "outer link").
Adversarial Metric Learning
In distinguishment stage, a metric is exhaustively learned to try its best to distinguish both the adversarial pairs and the original training pairs.
Pelee: A Real-Time Object Detection System on Mobile Devices
In this study, we propose an efficient architecture named PeleeNet, which is built with conventional convolution instead.
Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures
Embedding methods which enforce a partial order or lattice structure over the concept space, such as Order Embeddings (OE) (Vendrov et al., 2016), are a natural way to model transitive relational data (e. g. entailment graphs).
Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks
In this work, we propose to learn universal user representations across multiple tasks for more e ective personalization.
Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)
Simultaneous modeling of the spatio-temporal variation patterns of brain functional network from 4D fMRI data has been an important yet challenging problem for the field of cognitive neuroscience and medical image analysis.
Shape Robust Text Detection with Progressive Scale Expansion Network
To address these problems, we propose a novel Progressive Scale Expansion Network (PSENet), designed as a segmentation-based detector with multiple predictions for each text instance.
Ranked #12 on
Scene Text Detection
on ICDAR 2017 MLT
Next Check-ins Prediction via History and Friendship on Location-Based Social Networks
In this paper, we propose a unified location prediction framework to integrate the effect of history check-in and the influence of social circles.
Adversarial Open-World Person Re-Identification
The main idea is learning to attack feature extractor on the target people by using GAN to generate very target-like images (imposters), and in the meantime the model will make the feature extractor learn to tolerate the attack by discriminative learning so as to realize group-based verification.
Few-Shot Charge Prediction with Discriminative Legal Attributes
Specifically, our model outperforms other baselines by more than 50{\%} in the few-shot scenario.
Multi-Estimator Full Left Ventricle Quantification through Ensemble Learning
The framework combines two 1st-level modules: direct estimation module and a segmentation module.
Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation
In this paper, we propose a novel joint Task-Recursive Learning (TRL) framework for the closing-loop semantic segmentation and monocular depth estimation tasks.
Ranked #76 on
Semantic Segmentation
on NYU Depth v2
Network Modeling and Pathway Inference from Incomplete Data ("PathInf")
In this work, we developed a network inference method from incomplete data ("PathInf") , as massive and non-uniformly distributed missing values is a common challenge in practical problems.
Triple Attention Mixed Link Network for Single Image Super Resolution
Single image super resolution is of great importance as a low-level computer vision task.
Deep Diabetologist: Learning to Prescribe Hyperglycemia Medications with Hierarchical Recurrent Neural Networks
In healthcare, applying deep learning models to electronic health records (EHRs) has drawn considerable attention.
Community Detection with Graph Neural Networks
This graph inference task can be recast as a node-wise graph classification problem, and, as such, computational detection thresholds can be translated in terms of learning within appropriate models.
Improving the Robustness of Speech Translation
Although neural machine translation (NMT) has achieved impressive progress recently, it is usually trained on the clean parallel data set and hence cannot work well when the input sentence is the production of the automatic speech recognition (ASR) system due to the enormous errors in the source.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Automated Segmentation of Cervical Nuclei in Pap Smear Images using Deformable Multi-path Ensemble Model
Pap smear testing has been widely used for detecting cervical cancers based on the morphology properties of cell nuclei in microscopic image.
Defense-VAE: A Fast and Accurate Defense against Adversarial Attacks
The proposed method is generic and can defend white-box and black-box attacks without the need of retraining the original CNN classifiers, and can further strengthen the defense by retraining CNN or end-to-end finetuning the whole pipeline.
Group-Attention Single-Shot Detector (GA-SSD): Finding Pulmonary Nodules in Large-Scale CT Images
In this paper, we propose a novel and effective abnormality detector implementing the attention mechanism and group convolution on 3D single-shot detector (SSD) called group-attention SSD (GA-SSD).
Computed Tomography (CT)
Finding Pulmonary Nodules In Large-Scale Ct Images
Do Subsampled Newton Methods Work for High-Dimensional Data?
Subsampled Newton methods approximate Hessian matrices through subsampling techniques, alleviating the cost of forming Hessian matrices but using sufficient curvature information.
Joint Optimization of Tree-based Index and Deep Model for Recommender Systems
The previous work Tree-based Deep Model (TDM) \cite{zhu2018learning} greatly improves recommendation accuracy using tree index.
Dynamic Feature Fusion for Semantic Edge Detection
In this work, we propose a novel dynamic feature fusion strategy that assigns different fusion weights for different input images and locations adaptively.
A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning
We propose and study a general framework for regularized Markov decision processes (MDPs) where the goal is to find an optimal policy that maximizes the expected discounted total reward plus a policy regularization term.
Selective Kernel Networks
A building block called Selective Kernel (SK) unit is designed, in which multiple branches with different kernel sizes are fused using softmax attention that is guided by the information in these branches.
Ranked #98 on
Image Classification
on CIFAR-100
(using extra training data)
Shape Robust Text Detection with Progressive Scale Expansion Network
Due to the fact that there are large geometrical margins among the minimal scale kernels, our method is effective to split the close text instances, making it easier to use segmentation-based methods to detect arbitrary-shaped text instances.
Ranked #12 on
Scene Text Detection
on SCUT-CTW1500
Non-Rigid Point Set Registration Networks
In contrast, the proposed point registration neural network (PR-Net) actively learns the registration pattern as a parametric function from a training dataset, consequently predict the desired geometric transformation to align a pair of point sets.
ConvLab: Multi-Domain End-to-End Dialog System Platform
We present ConvLab, an open-source multi-domain end-to-end dialog system platform, that enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches, ranging from conventional pipeline systems to end-to-end neural models, in common environments.
Inpatient2Vec: Medical Representation Learning for Inpatients
In addition, for unordered medical activity set, existing medical RL methods utilize a simple pooling strategy, which would result in indistinguishable contributions among the activities for learning.
Smoothing the Geometry of Probabilistic Box Embeddings
However, the hard edges of the boxes present difficulties for standard gradient based optimization; that work employed a special surrogate function for the disjoint case, but we find this method to be fragile.
Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks
The Convolutional Neural Networks (CNNs) generate the feature representation of complex objects by collecting hierarchical and different parts of semantic sub-features.
Ranked #739 on
Image Classification
on ImageNet
Towards Photo-Realistic Visible Watermark Removal with Conditional Generative Adversarial Networks
Visible watermark plays an important role in image copyright protection and the robustness of a visible watermark to an attack is shown to be essential.
Coherent Point Drift Networks: Unsupervised Learning of Non-Rigid Point Set Registration
In contrast to previous efforts (e. g. coherent point drift), CPD-Net can learn displacement field function to estimate geometric transformation from a training dataset, consequently, to predict the desired geometric transformation for the alignment of previously unseen pairs without any additional iterative optimization process.
Cross-view Relation Networks for Mammogram Mass Detection
Mammogram is the most effective imaging modality for the mass lesion detection of breast cancer at the early stage.
On the Convergence of FedAvg on Non-IID Data
In this paper, we analyze the convergence of \texttt{FedAvg} on non-iid data and establish a convergence rate of $\mathcal{O}(\frac{1}{T})$ for strongly convex and smooth problems, where $T$ is the number of SGDs.
ASCNet: Adaptive-Scale Convolutional Neural Networks for Multi-Scale Feature Learning
Such pixel-level dilation rates produce optimal receptive fields so that the information of objects with different sizes can be extracted at the corresponding scale.
Scalable Semi-Supervised SVM via Triply Stochastic Gradients
Specifically, to handle two types of data instances involved in S$^3$VM, TSGS$^3$VM samples a labeled instance and an unlabeled instance as well with the random features in each iteration to compute a triply stochastic gradient.
FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation
In this paper, we are interested in few-shot object segmentation where the number of annotated training examples are limited to 5 only.
Ranked #20 on
Few-Shot Semantic Segmentation
on FSS-1000 (5-shot)
Quadruply Stochastic Gradients for Large Scale Nonlinear Semi-Supervised AUC Optimization
To address this problem, in this paper, we propose a novel scalable quadruply stochastic gradient algorithm (QSG-S2AUC) for nonlinear semi-supervised AUC optimization.
Neural Image Compression and Explanation
Explaining the prediction of deep neural networks (DNNs) and semantic image compression are two active research areas of deep learning with a numerous of applications in decision-critical systems, such as surveillance cameras, drones and self-driving cars, where interpretable decision is critical and storage/network bandwidth is limited.
Directionally Constrained Fully Convolutional Neural Network For Airborne Lidar Point Cloud Classification
In this paper, we proposed a directionally constrained fully convolutional neural network (D-FCN) that can take the original 3D coordinates and LiDAR intensity as input; thus, it can directly apply to unstructured 3D point clouds for semantic labeling.
Invasiveness Prediction of Pulmonary Adenocarcinomas Using Deep Feature Fusion Networks
In this study, we explore the fusion of the two kinds of features and claim that radiomics features can be complementary to deep-learning features.
Predicting Alzheimer's Disease by Hierarchical Graph Convolution from Positron Emission Tomography Imaging
Imaging-based early diagnosis of Alzheimer Disease (AD) has become an effective approach, especially by using nuclear medicine imaging techniques such as Positron Emission Topography (PET).
Improving One-shot NAS by Suppressing the Posterior Fading
In this paper, we analyse existing weight sharing one-shot NAS approaches from a Bayesian point of view and identify the posterior fading problem, which compromises the effectiveness of shared weights.
Multi-label Detection and Classification of Red Blood Cells in Microscopic Images
As the six networks are trained with image patches consisting of both individual cells and touching/overlapping cells, they can effectively recognize cell types that are presented in multi-instance image samples.
Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification
Based on this convolution module, we further developed a multi-scale fully convolutional neural network with downsampling and upsampling blocks to enable hierarchical point feature learning.
Communication-Efficient Local Decentralized SGD Methods
Recently, the technique of local updates is a powerful tool in centralized settings to improve communication efficiency via periodical communication.
Prediction stability as a criterion in active learning
Recent breakthroughs made by deep learning rely heavily on large number of annotated samples.
A General Early-Stopping Module for Crowdsourced Ranking
The number of microtasks depends on the budget allocated for the problem.
An End-to-End Deep RL Framework for Task Arrangement in Crowdsourcing Platforms
In this paper, we propose a Deep Reinforcement Learning (RL) framework for task arrangement, which is a critical problem for the success of crowdsourcing platforms.
Generative adversarial networks (GAN) based efficient sampling of chemical space for inverse design of inorganic materials
The percentage of chemically valid (charge neutral and electronegativity balanced) samples out of all generated ones reaches 84. 5% by our GAN when trained with materials from ICSD even though no such chemical rules are explicitly enforced in our GAN model, indicating its capability to learn implicit chemical composition rules.
Arbicon-Net: Arbitrary Continuous Geometric Transformation Networks for Image Registration
To address this issue, we present an end-to-end trainable deep neural networks, named Arbitrary Continuous Geometric Transformation Networks (Arbicon-Net), to directly predict the dense displacement field for pairwise image alignment.
Safe Sample Screening for Robust Support Vector Machine
To address this challenge, in this paper, we propose two safe sample screening rules for RSVM based on the framework of concave-convex procedure (CCCP).
Time-constrained Adaptive Influence Maximization
The well-known influence maximization problem aims at maximizing the influence of one information cascade in a social network by selecting appropriate seed users prior to the diffusion process.
Social and Information Networks
ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems
We present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems.
Representing Joint Hierarchies with Box Embeddings
Box Embeddings [Vilnis et al., 2018, Li et al., 2019] represent concepts with hyperrectangles in $n$-dimensional space and are shown to be capable of modeling tree-like structures efficiently by training on a large subset of the transitive closure of the WordNet hypernym graph.
Communication-Efficient Distributed SVD via Local Power Iterations
As a practical surrogate of OPT, sign-fixing, which uses a diagonal matrix with $\pm 1$ entries as weights, has better computation complexity and stability in experiments.
Adversarial Multimodal Representation Learning for Click-Through Rate Prediction
Finally, we achieve the multimodal item representations by combining both modality-specific and modality-invariant representations.
TEDL: A Text Encryption Method Based on Deep Learning
Firstly, both communication parties establish a word vector table by training a deep learning model according to specified hyperparameters.
Airborne LiDAR Point Cloud Classification with Graph Attention Convolution Neural Network
To achieve point cloud classification, previous studies proposed point cloud deep learning models that can directly process raw point clouds based on PointNet-like architectures.
A Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label Learning
In aligning, we characterize the global and local structures of multiple labels to be high-rank and low-rank, respectively.
One-Shot Object Detection without Fine-Tuning
Deep learning has revolutionized object detection thanks to large-scale datasets, but their object categories are still arguably very limited.
Convolutional Neural Network for Behavioral Modeling and Predistortion of Wideband Power Amplifiers
We design a pre-designed filter using the convolutional layer to extract the basis functions required for the PA forward or reverse modeling.
Gait Recognition via Semi-supervised Disentangled Representation Learning to Identity and Covariate Features
Existing gait recognition approaches typically focus on learning identity features that are invariant to covariates (e. g., the carrying status, clothing, walking speed, and viewing angle) and seldom involve learning features from the covariate aspect, which may lead to failure modes when variations due to the covariate overwhelm those due to the identity.
Height estimation from single aerial images using a deep ordinal regression network
Previous researches have extensively studied the problem of height estimation from aerial images based on stereo or multi-view image matching.
FGCRec: Fine-Grained Geographical Characteristics Modeling for Point-of-Interest Recommendation
With the popularity of location-based social networks (LBSNs), Point-of-Interest (POI) recommendation has become an essential location-based service to help people explore novel locations.
Modeling Discourse Structure for Document-level Neural Machine Translation
Finally, we combine the discourse structure information with the word embedding before it is fed into the encoder.
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
Specifically, we merge the quality estimation into the class prediction vector to form a joint representation of localization quality and classification, and use a vector to represent arbitrary distribution of box locations.
Ranked #104 on
Object Detection
on COCO test-dev
CAST: A Correlation-based Adaptive Spectral Clustering Algorithm on Multi-scale Data
We study the problem of applying spectral clustering to cluster multi-scale data, which is data whose clusters are of various sizes and densities.
Geometry-Aware Segmentation of Remote Sensing Images via Implicit Height Estimation
Recent studies have shown the benefits of using additional elevation data (e. g., DSM) for enhancing the performance of the semantic segmentation of aerial images.
Unsupervised Learning of 3D Point Set Registration
Moreover, for a pair of source and target point sets, existing deep learning mechanisms require explicitly designed encoders to extract both deep spatial features from unstructured point clouds and their spatial correlation representation, which is further fed to a decoder to regress the desired geometric transformation for point set alignment.
Few-shot Object Detection on Remote Sensing Images
In this paper, we introduce a few-shot learning-based method for object detection on remote sensing images where only a few annotated samples are provided for the unseen object categories.
Unsupervised Learning of Global Registration of Temporal Sequence of Point Clouds
Global registration of point clouds aims to find an optimal alignment of a sequence of 2D or 3D point sets.
Disentangling User Interest and Conformity for Recommendation with Causal Embedding
We further demonstrate that the learned embeddings successfully capture the desired causes, and show that DICE guarantees the robustness and interpretability of recommendation.
DeepTracking-Net: 3D Tracking with Unsupervised Learning of Continuous Flow
In this paper, we aim at handling the problem of 3D tracking, which provides the tracking of the consecutive frames of 3D shapes.
3DMotion-Net: Learning Continuous Flow Function for 3D Motion Prediction
To approach this problem, we propose a self-supervised approach that leverages the power of the deep neural network to learn a continuous flow function of 3D point clouds that can predict temporally consistent future motions and naturally bring out the correspondences among consecutive point clouds at the same time.
Xiaomi's Submissions for IWSLT 2020 Open Domain Translation Task
This paper describes the Xiaomi{'}s submissions to the IWSLT20 shared open domain translation task for Chinese{\textless}-{\textgreater}Japanese language pair.
Simulating multi-exit evacuation using deep reinforcement learning
We propose a multi-exit evacuation simulation based on Deep Reinforcement Learning (DRL), referred to as the MultiExit-DRL, which involves in a Deep Neural Network (DNN) framework to facilitate state-to-action mapping.
FGRec: A Fine-Grained Point-of-Interest Recommendation Framework by Capturing Intrinsic Influences
Recent studies mainly utilize social information, categorical information and/or geographical information to supplement the highly sparse check-in data.
GP-Aligner: Unsupervised Non-rigid Groupwise Point Set Registration Based On Optimized Group Latent Descriptor
More specifically, for a given group we first define an optimizable Group Latent Descriptor (GLD) to characterize the gruopwise relationship among a group of point sets.
Robust Image Matching By Dynamic Feature Selection
Recent works introduce convolutional neural networks (CNNs) to extract high-level feature maps and find correspondences through feature matching.
Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data
In this paper, we focus on nonlinear learning with kernels, and propose a federated doubly stochastic kernel learning (FDSKL) algorithm for vertically partitioned data.
Object Detection in the Context of Mobile Augmented Reality
Ordinary object detection approaches process information from the images only, and they are oblivious to the camera pose with regard to the environment and the scale of the environment.
Unsupervised Partial Point Set Registration via Joint Shape Completion and Registration
To bridge the performance gaps between partial point set registration with full point set registration, we proposed to incorporate a shape completion network to benefit the registration process.
Looking Beyond Sentence-Level Natural Language Inference for Downstream Tasks
In recent years, the Natural Language Inference (NLI) task has garnered significant attention, with new datasets and models achieving near human-level performance on it.
Deep-3DAligner: Unsupervised 3D Point Set Registration Network With Optimizable Latent Vector
Point cloud registration is the process of aligning a pair of point sets via searching for a geometric transformation.
Reading Comprehension as Natural Language Inference: A Semantic Analysis
We transform the one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms.
3D Meta Point Signature: Learning to Learn 3D Point Signature for 3D Dense Shape Correspondence
In this paper, we propose a novel meta-learning based 3D point signature model, named 3Dmetapointsignature (MEPS) network, that is capable of learning robust point signatures in 3D shapes.
3D Meta-Registration: Learning to Learn Registration of 3D Point Clouds
In this paper, we propose a meta-learning based 3D registration model, named 3D Meta-Registration, that is capable of rapidly adapting and well generalizing to new 3D registration tasks for unseen 3D point clouds.
Finding the Near Optimal Policy via Adaptive Reduced Regularization in MDPs
Regularized MDPs serve as a smooth version of original MDPs.
PC-GAIN: Pseudo-label Conditional Generative Adversarial Imputation Networks for Incomplete Data
Further, this classifier is incorporated into the generative adversarial framework to help the generator to yield higher quality imputation results.
Retrieving and ranking short medical questions with two stages neural matching model
Internet hospital is a rising business thanks to recent advances in mobile web technology and high demand of health care services.
Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection
Such a property makes the distribution statistics of a bounding box highly correlated to its real localization quality.
Ranked #26 on
Object Detection
on COCO-O
Deep Metric Learning-based Image Retrieval System for Chest Radiograph and its Clinical Applications in COVID-19
These results demonstrate our deep metric learning based image retrieval model is highly efficient in the CXR retrieval, diagnosis and prognosis, and thus has great clinical value for the treatment and management of COVID-19 patients.
Neuron-level Structured Pruning using Polarization Regularizer
Experimentally, we show that structured pruning using polarization regularizer achieves much better results than using L1 regularizer.
Reading Comprehension as Natural Language Inference:A Semantic Analysis
We transform one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms.
Physics Guided Machine Learning Methods for Hydrology
The goal of this work is to incorporate our understanding of physical processes and constraints in hydrology into machine learning algorithms, and thus bridge the performance gap while reducing the need for large amounts of data compared to traditional data-driven approaches.
Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling
To strike a balance in this trade-off, we propose Elapsed-Time Sampling Delayed Feedback Model (ES-DFM), which models the relationship between the observed conversion distribution and the true conversion distribution.
Modeling Homophone Noise for Robust Neural Machine Translation
In this paper, we propose a robust neural machine translation (NMT) framework.
Cost-Effective Federated Learning Design
In this paper, we analyze how to design adaptive FL that optimally chooses these essential control variables to minimize the total cost while ensuring convergence.
Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks
A heterogeneous information network (HIN) has as vertices objects of different types and as edges the relations between objects, which are also of various types.
Box-To-Box Transformation for Modeling Joint Hierarchies
In Patel et al. (2020), the authors demonstrate that only the transitive reduction is required, and further extend box embeddings to capture joint hierarchies by augmenting the graph with new nodes.
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and More
We show that our Generative MMC (GMMC) can be trained discriminatively, generatively, or jointly for image classification and generation.
Towards Cross-Modal Forgery Detection and Localization on Live Surveillance Videos
Traditional video forensics approaches can detect and localize forgery traces in each video frame using computationally-expensive spatial-temporal analysis, while falling short in real-time verification of live video feeds.
Time Series Analysis
Video Forensics
Cryptography and Security
Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications
In this work, we study a novel class of projection-based algorithms for linearly constrained problems (LCPs) which have a lot of applications in statistics, optimization, and machine learning.
Bridging Unpaired Facial Photos And Sketches By Line-drawings
In this paper, we propose a novel method to learn face sketch synthesis models by using unpaired data.
SceneRec: Scene-Based Graph Neural Networks for Recommender Systems
Collaborative filtering has been largely used to advance modern recommender systems to predict user preference.
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
Unlike the recently-proposed Transformer model (e. g., ViT) that is specially designed for image classification, we propose Pyramid Vision Transformer~(PVT), which overcomes the difficulties of porting Transformer to various dense prediction tasks.
Ranked #5 on
Semantic Segmentation
on SynPASS
vrCAPTCHA: Exploring CAPTCHA Designs in Virtual Reality
With the popularity of online access in virtual reality (VR) devices, it will become important to investigate exclusive and interactive CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) designs for VR devices.
Human-Computer Interaction
FedPower: Privacy-Preserving Distributed Eigenspace Estimation
The low communication power and the possible privacy breaches of data make the computation of eigenspace challenging.
Structure-Enhanced Meta-Learning For Few-Shot Graph Classification
Graph classification is a highly impactful task that plays a crucial role in a myriad of real-world applications such as molecular property prediction and protein function prediction. Aiming to handle the new classes with limited labeled graphs, few-shot graph classification has become a bridge of existing graph classification solutions and practical usage. This work explores the potential of metric-based meta-learning for solving few-shot graph classification. We highlight the importance of considering structural characteristics in the solution and propose a novel framework which explicitly considers global structure and local structure of the input graph.
Development and Validation of a Deep Learning Model for Prediction of Severe Outcomes in Suspected COVID-19 Infection
Predictive risk scores for COVID-19 severe outcomes ("CO-RISK" score) were derived from model output and evaluated on the testing dataset, as well as compared to human performance.
Towards Multi-Scale Style Control for Expressive Speech Synthesis
This paper introduces a multi-scale speech style modeling method for end-to-end expressive speech synthesis.
Deep Attributed Network Representation Learning via Attribute Enhanced Neighborhood
In this paper, we propose a deep attributed network representation learning via attribute enhanced neighborhood (DANRL-ANE) model to improve the robustness and effectiveness of node representations.
PAN++: Towards Efficient and Accurate End-to-End Spotting of Arbitrarily-Shaped Text
By systematically comparing with existing scene text representations, we show that our kernel representation can not only describe arbitrarily-shaped text but also well distinguish adjacent text.
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural Networks
Neural networks are susceptible to artificially designed adversarial perturbations.
Ranked #1 on
Adversarial Attack
on CIFAR-10
Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation
A neural multimodal machine translation (MMT) system is one that aims to perform better translation by extending conventional text-only translation models with multimodal information.
Improving Tree-Structured Decoder Training for Code Generation via Mutual Learning
However, such a decoder only exploits the preorder traversal based preceding actions, which are insufficient to ensure correct action predictions.
The Image Local Autoregressive Transformer
To address these limitations, we propose a novel model -- image Local Autoregressive Transformer (iLAT), to better facilitate the locally guided image synthesis.
Augmented 2D-TAN: A Two-stage Approach for Human-centric Spatio-Temporal Video Grounding
We propose an effective two-stage approach to tackle the problem of language-based Human-centric Spatio-Temporal Video Grounding (HC-STVG) task.
PVT v2: Improved Baselines with Pyramid Vision Transformer
We hope this work will facilitate state-of-the-art Transformer researches in computer vision.
Ranked #23 on
Object Detection
on COCO-O
A More Compact Object Detector Head Network with Feature Enhancement and Relational Reasoning
Without losing generality, we can also build a more lighter head network for other multi-stage detectors by assembling our method.
THE DCASE 2021 CHALLENGE TASK 6 SYSTEM: AUTOMATED AUDIO CAPTIONING WITH WEAKLY SUPERVISED PRE-TRAING AND WORD SELECTION METHODS
Our solution focuses on solving two problems in automated audio captioning: data insufficiency and word selection indeterminacy.
Ranked #1 on
Audio captioning
on Clotho
(using extra training data)
Learn to Learn Metric Space for Few-Shot Segmentation of 3D Shapes
To achieve this, we treat the shape segmentation as a point labeling problem in the metric space.
RigNet: Repetitive Image Guided Network for Depth Completion
However, blurry guidance in the image and unclear structure in the depth still impede the performance of the image guided frameworks.
Ranked #2 on
Depth Completion
on KITTI Depth Completion
HITSZ-HLT at SemEval-2021 Task 5: Ensemble Sequence Labeling and Span Boundary Detection for Toxic Span Detection
This paper presents the winning system that participated in SemEval-2021 Task 5: Toxic Spans Detection.
Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark
Monocular depth estimation aims at predicting depth from a single image or video.
Statistical Estimation and Inference via Local SGD in Federated Learning
Both the methods are communication efficient and applicable to online data.
Cost-Effective Federated Learning in Mobile Edge Networks
Federated learning (FL) is a distributed learning paradigm that enables a large number of mobile devices to collaboratively learn a model under the coordination of a central server without sharing their raw data.
Fine-Grained Few Shot Learning with Foreground Object Transformation
As a data augmentation method, FOT can be conveniently applied to any existing few shot learning algorithm and greatly improve its performance on FG-FSL tasks.
Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrology
Furthermore, we show that KGSSL is relatively more robust to distortion than baseline methods, and outperforms the baseline model by 35\% when plugging in KGSSL inferred characteristics.
Interpretable Multi-hop Reasoning for Forecasting Future Links on Temporal Knowledge Graphs
The interpretability of the current temporal KG forecasting models is manifested in providing the reasoning paths.
Student Helping Teacher: Teacher Evolution via Self-Knowledge Distillation
Knowledge distillation usually transfers the knowledge from a pre-trained cumbersome teacher network to a compact student network, which follows the classical teacher-teaching-student paradigm.
Feasible Architecture for Quantum Fully Convolutional Networks
Fully convolutional networks are robust in performing semantic segmentation, with many applications from signal processing to computer vision.
StARformer: Transformer with State-Action-Reward Representations for Visual Reinforcement Learning
Reinforcement Learning (RL) can be considered as a sequence modeling task: given a sequence of past state-action-reward experiences, an agent predicts a sequence of next actions.
Unified Style Transfer
Currently, it is hard to compare and evaluate different style transfer algorithms due to chaotic definitions of style and the absence of agreed objective validation methods in the study of style transfer.
Video Instance Segmentation by Instance Flow Assembly
Instance segmentation is a challenging task aiming at classifying and segmenting all object instances of specific classes.
Improved Loss Function-Based Prediction Method of Extreme Temperatures in Greenhouses
By increasing the weight of extreme temperature samples and reducing the possibility of misjudging extreme temperature as normal, the proposed loss function can enhance the prediction results in extreme situations.
Generative Dynamic Patch Attack
Extensive experiments on VGGFace, Traffic Sign and ImageNet show that GDPA achieves higher attack success rates than state-of-the-art patch attacks, while adversarially trained model with GDPA demonstrates superior robustness to adversarial patch attacks than competing methods.
Automated Pulmonary Embolism Detection from CTPA Images Using an End-to-End Convolutional Neural Network
We have evaluated our approach using the 20 CTPA test dataset from the PE challenge, achieving a sensitivity of 78. 9%, 80. 7% and 80. 7% at 2 false positives per volume at 0mm, 2mm and 5mm localization error, which is superior to the state-of-the-art methods.
Reinforcement Learning Enhanced Explainer for Graph Neural Networks
To address these issues, we propose a RL-enhanced GNN explainer, RG-Explainer, which consists of three main components: starting point selection, iterative graph generation and stopping criteria learning.
Shallow geothermal energy potential for heating and cooling of buildings with regeneration under climate change scenarios
Shallow ground-source heat pumps (GSHPs) are a promising technology for contributing to the decarbonisation of the energy sector.
Hybrid Instance-aware Temporal Fusion for Online Video Instance Segmentation
Based on this representation, we introduce a cropping-free temporal fusion approach to model the temporal consistency between video frames.
Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-guided Feature Imitation
Based on the two observations, we propose Rank Mimicking (RM) and Prediction-guided Feature Imitation (PFI) for distilling one-stage detectors, respectively.
A Statistical Analysis of Polyak-Ruppert Averaged Q-learning
We study Q-learning with Polyak-Ruppert averaging in a discounted Markov decision process in synchronous and tabular settings.
Scalable Deep Graph Clustering with Random-walk based Self-supervised Learning
Though other methods (particularly those based on Laplacian Smoothing) have reported better accuracy, a fundamental limitation of all the work is a lack of scalability.
Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems
As the deep learning techniques have expanded to real-world recommendation tasks, many deep neural network based Collaborative Filtering (CF) models have been developed to project user-item interactions into latent feature space, based on various neural architectures, such as multi-layer perceptron, auto-encoder and graph neural networks.
Forgery Attack Detection in Surveillance Video Streams Using Wi-Fi Channel State Information
The cybersecurity breaches expose surveillance video streams to forgery attacks, under which authentic streams are falsified to hide unauthorized activities.
Multi-modal Sensor Fusion for Auto Driving Perception: A Survey
Multi-modal fusion is a fundamental task for the perception of an autonomous driving system, which has recently intrigued many researchers.
Benchmarking of DL Libraries and Models on Mobile Devices
Deploying deep learning (DL) on mobile devices has been a notable trend in recent years.
WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation Models
Motivated by the observation that model-free methods like behavioral retargeting (BR) and item-based collaborative filtering (ItemCF) hit different parts of the user-item relevance compared to neural sequential recommendation models, we propose a novel model-agnostic training approach called WSLRec, which adopts a three-stage framework: pre-training, top-$k$ mining, and fine-tuning.
A density peaks clustering algorithm with sparse search and K-d tree
It is proved that the algorithm can effectively reduce the computational complexity of the original DPC from $O(n^2K)$ to $O(n(n^{1-1/K}+k))$.
Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal Information
Therefore, it is helpful to leverage additional information, e. g., the locations and dates for data shooting, which can be easily accessible but rarely exploited.
RecursiveMix: Mixed Learning with History
In semantic segmentation, RM also surpasses the baseline and CutMix by 1. 9 and 1. 1 mIoU points under UperNet on ADE20K, respectively.
EEG based Emotion Recognition: A Tutorial and Review
Hence, in this paper, we review from the perspective of researchers who try to take the first step on this topic.
Multi-Modal Masked Pre-Training for Monocular Panoramic Depth Completion
To deal with the PDC task, we train a deep network that takes both depth and image as inputs for the dense panoramic depth recovery.
A Learning Convolutional Neural Network Approach for Network Robustness Prediction
Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors; 2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability; 3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations; 4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures.
Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search
In this paper, we propose a new perspective for context-aware users' behavior modeling by including the whole page-wisely exposed products and the corresponding feedback as contextualized page-wise feedback sequence.
PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised Object Detection
Specifically, for pseudo labeling, existing works only focus on the classification score yet fail to guarantee the localization precision of pseudo boxes; For consistency training, the widely adopted random-resize training only considers the label-level consistency but misses the feature-level one, which also plays an important role in ensuring the scale invariance.
An End-to-end Chinese Text Normalization Model based on Rule-guided Flat-Lattice Transformer
Inspired by Flat-LAttice Transformer (FLAT), we propose an end-to-end Chinese text normalization model, which accepts Chinese characters as direct input and integrates expert knowledge contained in rules into the neural network, both contribute to the superior performance of proposed model for the text normalization task.
Speech Emotion Recognition with Global-Aware Fusion on Multi-scale Feature Representation
Speech Emotion Recognition (SER) is a fundamental task to predict the emotion label from speech data.
Lexical Knowledge Internalization for Neural Dialog Generation
We propose knowledge internalization (KI), which aims to complement the lexical knowledge into neural dialog models.
A Neural Network Architecture for Program Understanding Inspired by Human Behaviors
In this paper, we consider human behaviors and propose the PGNN-EK model that consists of two main components.
Weakly Supervised Text Classification using Supervision Signals from a Language Model
In this paper, we propose to query a masked language model with cloze style prompts to obtain supervision signals.
Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
Further, for other homophilous nodes excluded in the neighborhood, they are ignored for information aggregation.
Ranked #2 on
Node Classification
on pokec
