Search Results for author:
Found 330 papers, 125 papers with code
Label Propagation from ImageNet to 3D Point Clouds
Our approach shows further major gains in accuracy when the training data from the target scenes is used, outperforming state-ofthe-art approaches with far better efficiency.
Learning to Rank Binary Codes
Binary codes have been widely used in vision problems as a compact feature representation to achieve both space and time advantages.
DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection
Contour detection serves as the basis of a variety of computer vision tasks such as image segmentation and object recognition.
Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs
Object skeleton is a useful cue for object detection, complementary to the object contour, as it provides a structural representation to describe the relationship among object parts.
A Powerful Generative Model Using Random Weights for the Deep Image Representation
To our knowledge this is the first demonstration of image representations using untrained deep neural networks.
Model-Driven Feed-Forward Prediction for Manipulation of Deformable Objects
A fully featured 3D model of the garment is constructed in real-time and volumetric features are then used to obtain the most similar model in the database to predict the object category and pose.
e-Distance Weighted Support Vector Regression
We propose a novel support vector regression approach called e-Distance Weighted Support Vector Regression (e-DWSVR). e-DWSVR specifically addresses two challenging issues in support vector regression: first, the process of noisy data; second, how to deal with the situation when the distribution of boundary data is different from that of the overall data.
DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images
By observing the relationship between the receptive field sizes of the different layers in the network and the skeleton scales they can capture, we introduce two scale-associated side outputs to each stage of the network.
A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image
Three-dimensional shape reconstruction of 2D landmark points on a single image is a hallmark of human vision, but is a task that has been proven difficult for computer vision algorithms.
A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans
Deep neural networks have been widely adopted for automatic organ segmentation from abdominal CT scans.
EmotioNet Challenge: Recognition of facial expressions of emotion in the wild
The second track tested the algorithms' ability to recognize emotion categories in images of facial expressions.
Deep Collaborative Learning for Visual Recognition
We formulate the function of a convolutional layer as learning a large visual vocabulary, and propose an alternative way, namely Deep Collaborative Learning (DCL), to reduce the computational complexity.
SORT: Second-Order Response Transform for Visual Recognition
In this paper, we reveal the importance and benefits of introducing second-order operations into deep neural networks.
Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection
This design is biologically-plausible, as it likes a human visual system to compare different possible segmentation solutions to address the ambiguous boundary issue.
Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model
The visual cues hinting at attributes can be strongly localized and inference of person attributes such as hair, backpack, shorts, etc., are highly dependent on the acquired view of the pedestrian.
Deep Neural Solver for Math Word Problems
This paper presents a deep neural solver to automatically solve math word problems.
Ranked #4 on
Math Word Problem Solving
on ALG514
Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation
The key innovation is a saliency transformation module, which repeatedly converts the segmentation probability map from the previous iteration as spatial weights and applies these weights to the current iteration.
Ranked #1 on
Pancreas Segmentation
on TCIA Pancreas-CT Dataset
Recognition of Action Units in the Wild With Deep Nets and a New Global-Local Loss
Most previous algorithms for the recognition of Action Units (AUs) were trained on a small number of sample images.
Deep Regression Forests for Age Estimation
Age estimation from facial images is typically cast as a nonlinear regression problem.
Ranked #6 on
Age Estimation
on FGNET
Web-Scale Responsive Visual Search at Bing
In this paper, we introduce a web-scale general visual search system deployed in Microsoft Bing.
A Fast Divide-and-Conquer Sparse Cox Regression
We propose a computationally and statistically efficient divide-and-conquer (DAC) algorithm to fit sparse Cox regression to massive datasets where the sample size $n_0$ is exceedingly large and the covariate dimension $p$ is not small but $n_0\gg p$.
Computation Applications
Multi-Scale Spatially-Asymmetric Recalibration for Image Classification
Convolution is spatially-symmetric, i. e., the visual features are independent of its position in the image, which limits its ability to utilize contextual cues for visual recognition.
Training Multi-organ Segmentation Networks with Sample Selection by Relaxed Upper Confident Bound
Based on the fact that very hard samples might have annotation errors, we propose a new sample selection policy, named Relaxed Upper Confident Bound (RUCB).
Semi-Supervised Multi-Organ Segmentation via Deep Multi-Planar Co-Training
In multi-organ segmentation of abdominal CT scans, most existing fully supervised deep learning algorithms require lots of voxel-wise annotations, which are usually difficult, expensive, and slow to obtain.
Abdominal multi-organ segmentation with organ-attention networks and statistical fusion
To address these challenges, we introduce a novel framework for multi-organ segmentation by using organ-attention networks with reverse connections (OAN-RCs) which are applied to 2D views, of the 3D CT volume, and output estimates which are combined by statistical fusion exploiting structural similarity.
Automatic Article Commenting: the Task and Dataset
Comments of online articles provide extended views and improve user engagement.
Resource Aware Person Re-identification across Multiple Resolutions
Not all people are equally easy to identify: color statistics might be enough for some cases while others might require careful reasoning about high- and low-level details.
Ranked #12 on
Person Re-Identification
on CUHK03 detected
Generative Adversarial Learning Towards Fast Weakly Supervised Detection
Without the need of annotating bounding boxes, the existing methods usually follow a two/multi-stage pipeline with an online compulsive stage to extract object proposals, which is an order of magnitude slower than fast fully supervised object detectors such as SSD [31] and YOLO [34].
Spectral Network Embedding: A Fast and Scalable Method via Sparsity
In this paper, we propose a $10\times \sim 100\times$ faster network embedding method, called Progle, by elegantly utilizing the sparsity property of online networks and spectral analysis.
Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory
Such models rely on insufficient information for generating a specific response since a certain query could be answered in multiple ways.
Translating a Math Word Problem to a Expression Tree
Moreover, we analyze the performance of three popular SEQ2SEQ models on the math word problem solving.
Deep Person Re-identification for Probabilistic Data Association in Multiple Pedestrian Tracking
We present a data association method for vision-based multiple pedestrian tracking, using deep convolutional features to distinguish between different people based on their appearances.
Anytime Stereo Image Depth Estimation on Mobile Devices
Many applications of stereo depth estimation in robotics require the generation of accurate disparity maps in real time under significant computational constraints.
Ranked #1 on
Stereo Depth Estimation
on KITTI2012
Translating a Math Word Problem to an Expression Tree
Moreover, we analyze the performance of three popular SEQ2SEQ models on the math word problem solving.
Robust Face Detection via Learning Small Faces on Hard Images
In this paper, we propose that the robustness of a face detector against hard faces can be improved by learning small faces on hard images.
Ranked #8 on
Face Detection
on WIDER Face (Hard)
A two-stage hybrid model by using artificial neural networks as feature construction algorithms
The hybrid model uses a very simple neural network structure as the new feature construction tool in the first stage, then the newly created features are used as the additional input variables in logistic regression in the second stage.
Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving
However, in this paper we argue that it is not the quality of the data but its representation that accounts for the majority of the difference.
3D Object Detection From Stereo Images
Autonomous Driving
+2
An Automatic Interaction Detection Hybrid Model for Bankcard Response Classification
In the first stage of the hybrid model, CHAID analysis is used to detect the possibly potential variable interactions.
A XGBoost risk model via feature selection and Bayesian hyper-parameter optimization
TPE optimization shows a superiority over RS since it results in a significantly higher accuracy and a marginally higher AUC, recall and F1 score.
Risk Prediction of Peer-to-Peer Lending Market by a LSTM Model with Macroeconomic Factor
Our study can broaden the applications of the LSTM algorithm by using it on the sequential P2P data and guide the investors in making investment strategies.
A Survey on Session-based Recommender Systems
In recent years, session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs.
A Dynamic Model Identification Package for the da Vinci Research Kit
We developed a dynamic model identification package for the dVRK, capable of modeling the parallelograms, springs, counterweight, and tendon couplings, which are inherent to the dVRK.
Robotics
Predicting class-imbalanced business risk using resampling, regularization, and model ensembling algorithms
We aim at developing and improving the imbalanced business risk modeling via jointly using proper evaluation criteria, resampling, cross-validation, classifier regularization, and ensembling techniques.
Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes
We developed and evaluated two machine learning models to classify abstracts as relevant to the penetrance (risk of cancer for germline mutation carriers) or prevalence of germline genetic mutations.
DDPNAS: Efficient Neural Architecture Search via Dynamic Distribution Pruning
With the proposed efficient network generation method, we directly obtain the optimal neural architectures on given constraints, which is practical for on-device models across diverse search spaces and constraints.
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
In this paper we provide substantial advances to the pseudo-LiDAR framework through improvements in stereo depth estimation.
3D Object Detection From Stereo Images
Autonomous Driving
+2
Multi-Scale Attentional Network for Multi-Focal Segmentation of Active Bleed after Pelvic Fractures
Trauma is the worldwide leading cause of death and disability in those younger than 45 years, and pelvic fractures are a major source of morbidity and mortality.
Modeling Intra-Relation in Math Word Problems with Different Functional Multi-Head Attentions
Several deep learning models have been proposed for solving math word problems (MWPs) automatically.
Ranked #13 on
Math Word Problem Solving
on Math23K
An Experimental-based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging
Underwater images play a key role in ocean exploration, but often suffer from severe quality degradation due to light absorption and scattering in water medium.
Deep Differentiable Random Forests for Age Estimation
Both of them connect split nodes to the top layer of convolutional neural networks (CNNs) and deal with inhomogeneous data by jointly learning input-dependent data partitions at the split nodes and age distributions at the leaf nodes.
A systematic review of fuzzing based on machine learning techniques
Next, this paper assesses the performance of the machine learning models based on the frequently used evaluation metrics.
Semi-Supervised Adversarial Monocular Depth Estimation
In this paper, we address the problem of monocular depth estimation when only a limited number of training image-depth pairs are available.
Hyper-Pairing Network for Multi-Phase Pancreatic Ductal Adenocarcinoma Segmentation
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with an overall five-year survival rate of 8%.
Motion Planning through Demonstration to Deal with Complex Motions in Assembly Process
Complex and skillful motions in actual assembly process are challenging for the robot to generate with existing motion planning approaches, because some key poses during the human assembly can be too skillful for the robot to realize automatically.
Hadamard Codebook Based Deep Hashing
Specifically, we utilize an off-the-shelf algorithm to generate a binary Hadamard codebook to satisfy the requirement of bit independence and bit balance, which subsequently serves as the desired outputs of the hash functions learning.
Metric Classification Network in Actual Face Recognition Scene
In this paper, we train a validation classifier to normalize the decision threshold, which means that the result can be obtained directly without replacing the threshold.
Applications of Generative Adversarial Models in Visual Search Reformulation
Query reformulation is the process by which a input search query is refined by the user to match documents outside the original top-n results.
LDLS: 3-D Object Segmentation Through Label Diffusion From 2-D Images
We obtain 2-D segmentation predictions by applying Mask-RCNN to the RGB image, and then link this image to a 3-D lidar point cloud by building a graph of connections among 3-D points and 2-D pixels.
Improving Open-Domain Dialogue Systems via Multi-Turn Incomplete Utterance Restoration
To facilitate the study of incomplete utterance restoration for open-domain dialogue systems, a large-scale multi-turn dataset Restoration-200K is collected and manually labeled with the explicit relation between an utterance and its context.
Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework
End-to-end sequence generation is a popular technique for developing open domain dialogue systems, though they suffer from the \textit{safe response problem}.
SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning
Few-shot learners aim to recognize new object classes based on a small number of labeled training examples.
Adaptive Portfolio by Solving Multi-armed Bandit via Thompson Sampling
Also, by constructing multiple strategic arms, we can obtain the optimal investment portfolio to adapt different investment periods.
Variational Structured Semantic Inference for Diverse Image Captioning
To model these two inherent diversities in image captioning, we propose a Variational Structured Semantic Inferring model (termed VSSI-cap) executed in a novel structured encoder-inferer-decoder schema.
Deep Distance Transform for Tubular Structure Segmentation in CT Scans
Tubular structure segmentation in medical images, e. g., segmenting vessels in CT scans, serves as a vital step in the use of computers to aid in screening early stages of related diseases.
Sequential Recommender Systems: Challenges, Progress and Prospects
The emerging topic of sequential recommender systems has attracted increasing attention in recent years. Different from the conventional recommender systems including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users preferences and item popularity over time.
Cross-dataset Training for Class Increasing Object Detection
Given two or more already labeled datasets that target for different object classes, cross-dataset training aims to detect the union of the different classes, so that we do not have to label all the classes for all the datasets.
Proposal Learning for Semi-Supervised Object Detection
two-stage object detectors) by training on both labeled and unlabeled data.
HRank: Filter Pruning using High-Rank Feature Map
The principle behind our pruning is that low-rank feature maps contain less information, and thus pruned results can be easily reproduced.
Temporal Convolutional Attention-based Network For Sequence Modeling
So we propose an exploratory architecture referred to Temporal Convolutional Attention-based Network (TCAN) which combines temporal convolutional network and attention mechanism.
Stylistic Dialogue Generation via Information-Guided Reinforcement Learning Strategy
To enable better balance between the content quality and the style, we introduce a new training strategy, know as Information-Guided Reinforcement Learning (IG-RL).
Prototype-to-Style: Dialogue Generation with Style-Aware Editing on Retrieval Memory
A stylistic response generator then takes the prototype and the desired language style as model input to obtain a high-quality and stylistic response.
The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection
Despite that response selection is naturally a learning-to-rank problem, most prior works take a point-wise view and train binary classifiers for this task: each response candidate is labeled either relevant (one) or irrelevant (zero).
Ranked #10 on
Conversational Response Selection
on E-commerce
End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection
Reliable and accurate 3D object detection is a necessity for safe autonomous driving.
Generate, Delete and Rewrite: A Three-Stage Framework for Improving Persona Consistency of Dialogue Generation
Maintaining a consistent personality in conversations is quite natural for human beings, but is still a non-trivial task for machines.
Graph Learning Approaches to Recommender Systems: A Review
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS).
Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning
This paper focuses on the active flow control of a computational fluid dynamics simulation over a range of Reynolds numbers using deep reinforcement learning (DRL).
Fluid Dynamics
A Dual-Dimer Method for Training Physics-Constrained Neural Networks with Minimax Architecture
Data sparsity is a common issue to train machine learning tools such as neural networks for engineering and scientific applications, where experiments and simulations are expensive.
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Images for Segmentation
To mitigate this shortcoming, we propose a novel group equivariant segmentation framework by encoding those inherent symmetries for learning more precise representations.
Train in Germany, Test in The USA: Making 3D Object Detectors Generalize
In the domain of autonomous driving, deep learning has substantially improved the 3D object detection accuracy for LiDAR and stereo camera data alike.
Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation
In this paper, we present a novel unsupervised domain adaptation (UDA) method, named Domain Adaptive Relational Reasoning (DARR), to generalize 3D multi-organ segmentation models to medical data collected from different scanners and/or protocols (domains).
The 'Letter' Distribution in the Chinese Language
In addition, we collected Chinese literature corpora for different historical periods from the Tang Dynasty to the present, and we dismantled the Chinese written language into three kinds of basic particles: characters, strokes and constructive parts.
Organ size increases with obesity and correlates with cancer risk
Obesity increases significantly cancer risk in various organs.
Recognizing Chinese Judicial Named Entity using BiLSTM-CRF
Named entity recognition (NER) plays an essential role in natural language processing systems.
DSU-net: Dense SegU-net for automatic head-and-neck tumor segmentation in MR images
In this paper, we propose a Dense SegU-net (DSU-net) framework for automatic NPC segmentation in MRI.
Contextualized Emotion Recognition in Conversation as Sequence Tagging
Emotion recognition in conversation (ERC) is an important topic for developing empathetic machines in a variety of areas including social opinion mining, health-care and so on.
Ranked #2 on
Emotion Recognition in Conversation
on DailyDialog
Emotion Classification
Emotion Recognition in Conversation
+1
Graph-to-Tree Learning for Solving Math Word Problems
While the recent tree-based neural models have demonstrated promising results in generating solution expression for the math word problem (MWP), most of these models do not capture the relationships and order information among the quantities well.
Ranked #10 on
Math Word Problem Solving
on Math23K
Wasserstein Distances for Stereo Disparity Estimation
Existing approaches to depth or disparity estimation output a distribution over a set of pre-defined discrete values.
Ranked #2 on
Stereo Depth Estimation
on KITTI2015
(three pixel error metric)
3D Object Detection From Stereo Images
Autonomous Driving
+5
srMO-BO-3GP: A sequential regularized multi-objective constrained Bayesian optimization for design applications
Finally, we couple the third GP along with the classical BO framework to promote the richness and diversity of the Pareto frontier by the exploitation and exploration acquisition function.
Deep Co-Training with Task Decomposition for Semi-Supervised Domain Adaptation
To integrate the strengths of the two classifiers, we apply the well-established co-training framework, in which the two classifiers exchange their high confident predictions to iteratively "teach each other" so that both classifiers can excel in the target domain.
Semi-supervised Domain Adaptation
Unsupervised Domain Adaptation
Interpretable Real-Time Win Prediction for Honor of Kings, a Popular Mobile MOBA Esport
With the rapid prevalence and explosive development of MOBA esports (Multiplayer Online Battle Arena electronic sports), much research effort has been devoted to automatically predicting game results (win predictions).
Auxiliary-task Based Deep Reinforcement Learning for Participant Selection Problem in Mobile Crowdsourcing
Therefore, it is crucial to design a participant selection algorithm that applies to different MCS systems to achieve multiple goals.
Improving Investment Suggestions for Peer-to-Peer (P2P) Lending via Integrating Credit Scoring into Profit Scoring
The studies have mainly focused on two categories to guide the lenders' investments: one aims at minimizing the risk of investment (i. e., the credit scoring perspective) while the other aims at maximizing the profit (i. e., the profit scoring perspective).
Developing and Improving Risk Models using Machine-learning Based Algorithms
The objective of this study is to develop a good risk model for classifying business delinquency by simultaneously exploring several machine learning based methods including regularization, hyper-parameter optimization, and model ensembling algorithms.
A Deep Framework for Cross-Domain and Cross-System Recommendations
Therefore, finding an accurate mapping of the latent factors across domains or systems is crucial to enhancing recommendation accuracy.
Double-Wing Mixture of Experts for Streaming Recommendations
In VRS-DWMoE, we first devise variational and reservoir-enhanced sampling to wisely complement new data with historical data, and thus address the user preference drift issue while capturing long-term user preferences.
Stratified and Time-aware Sampling based Adaptive Ensemble Learning for Streaming Recommendations
To address these problems, we propose a Stratified and Time-aware Sampling based Adaptive Ensemble Learning framework, called STS-AEL, to improve the accuracy of streaming recommendations.
Enhancing Dialogue Generation via Multi-Level Contrastive Learning
Most of the existing works for dialogue generation are data-driven models trained directly on corpora crawled from websites.
Profile Consistency Identification for Open-domain Dialogue Agents
Maintaining a consistent attribute profile is crucial for dialogue agents to naturally converse with humans.
Rotated Binary Neural Network
In this paper, for the first time, we explore the influence of angular bias on the quantization error and then introduce a Rotated Binary Neural Network (RBNN), which considers the angle alignment between the full-precision weight vector and its binarized version.
ASMFS: Adaptive-Similarity-based Multi-modality Feature Selection for Classification of Alzheimer's Disease
With the increasing amounts of high-dimensional heterogeneous data to be processed, multi-modality feature selection has become an important research direction in medical image analysis.
Deep Learning Analysis and Age Prediction from Shoeprints
We collected 100, 000 shoeprints of subjects ranging from 7 to 80 years old and used the data to develop a deep learning end-to-end model ShoeNet to analyze age-related patterns and predict age.
Learning Efficient GANs for Image Translation via Differentiable Masks and co-Attention Distillation
The latter simultaneously distills informative attention maps from both the generator and discriminator of a pre-trained model to the searched generator, effectively stabilizing the adversarial training of our light-weight model.
Inter-slice Context Residual Learning for 3D Medical Image Segmentation
In this paper, we propose the 3D context residual network (ConResNet) for the accurate segmentation of 3D medical images.
Ferryman at SemEval-2020 Task 7: Ensemble Model for Assessing Humor in Edited News Headlines
Natural language processing (NLP) has been applied to various fields including text classification and sentiment analysis.
Ferryman at SemEval-2020 Task 12: BERT-Based Model with Advanced Improvement Methods for Multilingual Offensive Language Identification
Furthermore, our team also ranked in the top 20 on the Sub-task A of other languages.
Ferryman as SemEval-2020 Task 5: Optimized BERT for Detecting Counterfactuals
The main purpose of this article is to state the effect of using different methods and models for counterfactual determination and detection of causal knowledge.
Complementary probe of dark matter blind spots by lepton colliders and gravitational waves
We study how to unravel the dark matter blind spots by phase transition gravitational waves in synergy with collider signatures at electroweak one-loop level taking the inert doublet model as an example.
High Energy Physics - Phenomenology
A multi-objective optimization framework for on-line ridesharing systems
The ultimate goal of ridesharing systems is to matchtravelers who do not have a vehicle with those travelers whowant to share their vehicle.
Predicting Events in MOBA Games: Prediction, Attribution, and Evaluation
The multiplayer online battle arena (MOBA) games have become increasingly popular in recent years.
Towards Scalable and Privacy-Preserving Deep Neural Network via Algorithmic-Cryptographic Co-design
In this paper, we propose SPNN - a Scalable and Privacy-preserving deep Neural Network learning framework, from algorithmic-cryptographic co-perspective.
Dialogue Response Selection with Hierarchical Curriculum Learning
As for IC, it progressively strengthens the model's ability in identifying the mismatching information between the dialogue context and a response candidate.
Ranked #3 on
Conversational Response Selection
on RRS
AC-VAE: Learning Semantic Representation with VAE for Adaptive Clustering
Experimental evaluations show that the proposed method outperforms state-of-the-art representation learning methods in terms of neighbor clustering accuracy.
A Two Sub-problem Decomposition for the Optimal Design of Filterless Optical Networks
The first type of subproblem relies on the generation of filterless subnetworks while the second one takes care of their wavelength assignment.
Problem Decomposition
Networking and Internet Architecture
PLUMENet: Efficient 3D Object Detection from Stereo Images
In this paper we propose a model that unifies these two tasks and performs them in the same metric space.
Network Pruning using Adaptive Exemplar Filters
Inspired by the face recognition community, we use a message passing algorithm Affinity Propagation on the weight matrices to obtain an adaptive number of exemplars, which then act as the preserved filters.
Spreading dynamics of a 2SIH2R, rumor spreading model in the homogeneous network
In the era of the rapid development of the Internet, the threshold for information spreading has become lower.
Social and Information Networks Probability
Can Predominant Credible Information Suppress Misinformation in Crises? Empirical Studies of Tweets Related to Prevention Measures during COVID-19
Therefore, this research investigates the temporal correlations between credible information and misinformation, and whether predominant credible information can suppress misinformation for two prevention measures (i. e. topics), i. e. wearing masks and social distancing using tweets collected from February 15 to June 30, 2020.
Quantum Transport in Two-Dimensional WS$_2$ with High-Efficiency Carrier Injection Through Indium Alloy Contacts
Here, temperature-dependent transfer length measurements are performed on chemical vapour deposition grown single-layer and bilayer WS$_2$ devices with indium alloy contacts.
Materials Science Mesoscale and Nanoscale Physics
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning.
Ranked #4 on
Medical Image Segmentation
on ACDC
Tensor modules over Witt superalgebras
$W_{m, n}$), where $P$ is a simple module over the Weyl superalgebra $K_{m, n}^+$ (resp.
Representation Theory
Non-Autoregressive Text Generation with Pre-trained Language Models
In this work, we show that BERT can be employed as the backbone of a NAG model to greatly improve performance.
CogDL: A Comprehensive Library for Graph Deep Learning
In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries.
Cross-Domain Recommendation: Challenges, Progress, and Prospects
To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed to leverage the relatively richer information from a richer domain to improve the recommendation performance in a sparser domain.
SpecTr: Spectral Transformer for Hyperspectral Pathology Image Segmentation
Hyperspectral imaging (HSI) unlocks the huge potential to a wide variety of applications relied on high-precision pathology image segmentation, such as computational pathology and precision medicine.
Distilling a Powerful Student Model via Online Knowledge Distillation
Besides, a self-distillation module is adopted to convert the feature map of deeper layers into a shallower one.
Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection
Self-driving cars must detect other vehicles and pedestrians in 3D to plan safe routes and avoid collisions.
Checkerboard Context Model for Efficient Learned Image Compression
To the best of our knowledge, this is the first exploration on parallelization-friendly spatial context model for learned image compression.
Sketch and Customize: A Counterfactual Story Generator
In the sketch stage, a skeleton is extracted by removing words which are conflict to the counterfactual condition, from the original ending.
Distributed Eco-Driving Algorithm of Vehicle Platoon Using Traffic Light and Road Slope Information
This paper investigates the problem of ecological driving (eco-driving) of vehicle platoons.
Black-Box Dissector: Towards Erasing-based Hard-Label Model Stealing Attack
We argue this is due to the lack of rich information in the probability prediction and the overfitting caused by hard labels.
ISTR: End-to-End Instance Segmentation with Transformers
However, such an upgrade is not applicable to instance segmentation, due to its significantly higher output dimensions compared to object detection.
Ranked #21 on
Instance Segmentation
on COCO test-dev
Deep Learning based Multi-modal Computing with Feature Disentanglement for MRI Image Synthesis
To take full advantage of the complementary information provided by different modalities, multi-modal MRI sequences are utilized as input.
Graph Learning based Recommender Systems: A Review
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS).
Optimal Estimator Design and Properties Analysis for Interconnected Systems with Asymmetric Information Structure
We found that there exists a critical probability such that the EEC is bounded if the delay probability is below the critical probability.
Neural Machine Translation with Monolingual Translation Memory
Second, the memory retriever and NMT model can be jointly optimized for the ultimate translation goal.
Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction
We instantiate both the global and the local classifiers by multiple instance learning (MIL), where the attention guidance, indicating roughly where the PDAC regions are, is the key to bridging them: For global MIL based normal/PDAC classification, attention serves as a weight for each instance (voxel) during MIL pooling, which eliminates the distraction from the background; For local MIL based semi-supervised PDAC segmentation, the attention guidance is inductive, which not only provides bag-level pseudo-labels to training data without per-voxel annotations for MIL training, but also acts as a proxy of an instance-level classifier.
You Only Compress Once: Towards Effective and Elastic BERT Compression via Exploit-Explore Stochastic Nature Gradient
Motivated by the necessity of efficient inference across various constraints on BERT, we propose a novel approach, YOCO-BERT, to achieve compress once and deploy everywhere.
The Medical Segmentation Decathlon
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data
Maintaining consistent personas is essential for dialogue agents.
Next-item Recommendations in Short Sessions
The obtained similar sessions are then utilized to complement and optimize the preference representation learned from the current short session by the local module for more accurate next-item recommendations in this short session.
A Unified Framework for Cross-Domain and Cross-System Recommendations
Moreover, to avoid negative transfer, we further propose a Personalized training strategy to minimize the embedding difference of common entities between a richer dataset and a sparser dataset, deriving three new models, i. e., GA-DTCDR-P, GA-MTCDR-P, and GA-CDR+CSR-P, for the three scenarios respectively.
ASR-GLUE: A New Multi-task Benchmark for ASR-Robust Natural Language Understanding
%To facilitate the research on ASR-robust general language understanding, In this paper, we propose ASR-GLUE benchmark, a new collection of 6 different NLU tasks for evaluating the performance of models under ASR error across 3 different levels of background noise and 6 speakers with various voice characteristics.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers
Over the last few years, there are a growing number of datasets and deep learning-based methods proposed for effectively solving MWPs.
Ranked #8 on
Math Word Problem Solving
on Math23K
Real World Robustness from Systematic Noise
Systematic error, which is not determined by chance, often refers to the inaccuracy (involving either the observation or measurement process) inherent to a system.
RobustART: Benchmarking Robustness on Architecture Design and Training Techniques
Thus, we propose RobustART, the first comprehensive Robustness investigation benchmark on ImageNet regarding ARchitecture design (49 human-designed off-the-shelf architectures and 1200+ networks from neural architecture search) and Training techniques (10+ techniques, e. g., data augmentation) towards diverse noises (adversarial, natural, and system noises).
Stylistic Retrieval-based Dialogue System with Unparallel Training Data
The ability of a dialog system to express consistent language style during conversations has a direct, positive impact on its usability and on user satisfaction.
OMPQ: Orthogonal Mixed Precision Quantization
Instead of solving a problem of the original integer programming, we propose to optimize a proxy metric, the concept of network orthogonality, which is highly correlated with the loss of the integer programming but also easy to optimize with linear programming.
MHFC: Multi-Head Feature Collaboration for Few-Shot Learning
Apply the trained FEM to acquire the novel data's features and recognize them.
Post-Training Quantization Is All You Need to Perform Cross-Platform Learned Image Compression
It has been witnessed that learned image compression has outperformed conventional image coding techniques and tends to be practical in industrial applications.
Fixed Neural Network Steganography: Train the images, not the network
Recent attempts at image steganography make use of advances in deep learning to train an encoder-decoder network pair to hide and retrieve secret messages in images.
HLIC: Harmonizing Optimization Metrics in Learned Image Compression by Reinforcement Learning
Learned image compression is making good progress in recent years.
WDCCNet: Weighted Double-Classifier Constraint Neural Network for Mammographic Image Classification
Our proposed method can be easily applied to an existing convolutional neural network to improve mammographic image classification performance.
Ranked #1 on
Suspicous (BIRADS 4,5)-no suspicous (BIRADS 1,2,3) per image classification
on InBreast
Image Classification
Suspicous (BIRADS 4,5)-no suspicous (BIRADS 1,2,3) per image classification
Exploring Dense Retrieval for Dialogue Response Selection
In this study, we present a solution to directly select proper responses from a large corpus or even a nonparallel corpus that only consists of unpaired sentences, using a dense retrieval model.
Training Neural Networks for Solving 1-D Optimal Piecewise Linear Approximation
Our main contributions are that we propose the theorems to characterize the optimal solution of the PWLA problem and present the LNN method for solving it.
Intriguing Findings of Frequency Selection for Image Deblurring
Blur was naturally analyzed in the frequency domain, by estimating the latent sharp image and the blur kernel given a blurry image.
Ranked #2 on
Deblurring
on RealBlur-R
Multi-initialization Optimization Network for Accurate 3D Human Pose and Shape Estimation
In the second stage, we design a mesh refinement transformer (MRT) to respectively refine each coarse reconstruction result via a self-attention mechanism.
Ranked #64 on
3D Human Pose Estimation
on 3DPW
(MPJPE metric)
External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation with Limited Data
The spleen is one of the most commonly injured solid organs in blunt abdominal trauma.
Protum: A New Method For Prompt Tuning Based on "[MASK]"
In response to the above issue, we propose a new \textbf{Pro}mpt \textbf{Tu}ning based on "[\textbf{M}ASK]" (\textbf{Protum}) method in this paper, which constructs a classification task through the information carried by the hidden layer of "[MASK]" tokens and then predicts the labels directly rather than the answer tokens.
Approximation of Images via Generalized Higher Order Singular Value Decomposition over Finite-dimensional Commutative Semisimple Algebra
Higher order singular value decomposition (HOSVD) extends the SVD and can approximate higher order data using sums of a few rank-one components.
A Survey on Retrieval-Augmented Text Generation
Recently, retrieval-augmented text generation attracted increasing attention of the computational linguistics community.
A Contrastive Framework for Neural Text Generation
Text generation is of great importance to many natural language processing applications.
Post-Training Quantization for Cross-Platform Learned Image Compression
It has been witnessed that learned image compression has outperformed conventional image coding techniques and tends to be practical in industrial applications.
Multi-View Fusion Transformer for Sensor-Based Human Activity Recognition
In this paper, to extract a better feature for advancing the performance, we propose a novel method, namely multi-view fusion transformer (MVFT) along with a novel attention mechanism.
FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition in Videos
Current benchmarks for facial expression recognition (FER) mainly focus on static images, while there are limited datasets for FER in videos.
ContrastMask: Contrastive Learning to Segment Every Thing
In this framework, annotated masks of seen categories and pseudo masks of unseen categories serve as a prior for contrastive learning, where features from the mask regions (foreground) are pulled together, and are contrasted against those from the background, and vice versa.
ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding
Recently, learned image compression techniques have achieved remarkable performance, even surpassing the best manually designed lossy image coders.
Ranked #1 on
Image Compression
on kodak
Weakly-Supervised Salient Object Detection Using Point Supervision
Then we develop a transformer-based point-supervised saliency detection model to produce the first round of saliency maps.
Adaptive Frequency Learning in Two-branch Face Forgery Detection
Face forgery has attracted increasing attention in recent applications of computer vision.
Practical Learned Lossless JPEG Recompression with Multi-Level Cross-Channel Entropy Model in the DCT Domain
JPEG is a popular image compression method widely used by individuals, data center, cloud storage and network filesystems.
Towards Lightweight Transformer via Group-wise Transformation for Vision-and-Language Tasks
Despite the exciting performance, Transformer is criticized for its excessive parameters and computation cost.
Edge-enhanced Feature Distillation Network for Efficient Super-Resolution
With the recently massive development in convolution neural networks, numerous lightweight CNN-based image super-resolution methods have been proposed for practical deployments on edge devices.
Language Models Can See: Plugging Visual Controls in Text Generation
MAGIC is a flexible framework and is theoretically compatible with any text generation tasks that incorporate image grounding.
Effectively Using Long and Short Sessions for Multi-Session-based Recommendations
The RNN often used in previous work is not suitable to process short sessions, because RNN only focuses on the sequential relationship, which we find is not the only relationship between items in short sessions.
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
A Correlation Information-based Spatiotemporal Network for Traffic Flow Forecasting
In this paper, based on the maximal information coefficient, we present two elaborate spatiotemporal representations, spatial correlation information (SCorr) and temporal correlation information (TCorr).
Ranked #1 on
Traffic Prediction
on HZME(outflow)
Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities
In recent years, sequential recommender systems (SRSs) and session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs to capture users' short-term but dynamic preferences for enabling more timely and accurate recommendations.
PO-ELIC: Perception-Oriented Efficient Learned Image Coding
In the past years, learned image compression (LIC) has achieved remarkable performance.
Automatic Prosody Annotation with Pre-Trained Text-Speech Model
Prosodic boundary plays an important role in text-to-speech synthesis (TTS) in terms of naturalness and readability.
Review Neural Networks about Image Transformation Based on IGC Learning Framework with Annotated Information
From the perspective of this framework, we review those subtasks and give a unified interpretation of various scenarios.
Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera
We propose Neural-DynamicReconstruction (NDR), a template-free method to recover high-fidelity geometry and motions of a dynamic scene from a monocular RGB-D camera.
Unsupervised Domain Adaptation through Shape Modeling for Medical Image Segmentation
Previous methods proposed Variational Autoencoder (VAE) based models to learn the distribution of shape for a particular organ and used it to automatically evaluate the quality of a segmentation prediction by fitting it into the learned shape distribution.
Multi-agent systems with CBF-based controllers -- collision avoidance and liveness from instability
Monte Carlo simulations show that decentralized, host-only control policies and CCS lack liveness while the PCCA policy performs as well as the Centralized.
Few-Shot Semantic Relation Prediction across Heterogeneous Graphs
In real-world scenarios, new semantic relations constantly emerge and they typically appear with only a few labeled data.
SHREC'22 Track: Sketch-Based 3D Shape Retrieval in the Wild
We define two SBSR tasks and construct two benchmarks consisting of more than 46, 000 CAD models, 1, 700 realistic models, and 145, 000 sketches in total.
Weakly Supervised Video Salient Object Detection via Point Supervision
Several works attempt to use scribble annotations to mitigate this problem, but point supervision as a more labor-saving annotation method (even the most labor-saving method among manual annotation methods for dense prediction), has not been explored.
Evaluating the Practicality of Learned Image Compression
On CPU, the latency of our implementation is comparable with JPEG XL.
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation
Automatic tumor or lesion segmentation is a crucial step in medical image analysis for computer-aided diagnosis.
Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions
Advances in perception for self-driving cars have accelerated in recent years due to the availability of large-scale datasets, typically collected at specific locations and under nice weather conditions.
Effidit: Your AI Writing Assistant
In Effidit, we significantly expand the capacities of a writing assistant by providing functions in five categories: text completion, error checking, text polishing, keywords to sentences (K2S), and cloud input methods (cloud IME).
Stochastic MPC with Dual Control for Autonomous Driving with Multi-Modal Interaction-Aware Predictions
The proposed approach is demonstrated on a longitudinal control example, with uncertainties in predictions of the autonomous and surrounding vehicles.
Trustworthy Recommender Systems
However, researchers lack a systematic overview and discussion of the literature in this novel and fast developing field of TRSs.
RIBAC: Towards Robust and Imperceptible Backdoor Attack against Compact DNN
Recently backdoor attack has become an emerging threat to the security of deep neural network (DNN) models.
Flexible Neural Image Compression via Code Editing
Neural image compression (NIC) has outperformed traditional image codecs in rate-distortion (R-D) performance.
S$^3$R: Self-supervised Spectral Regression for Hyperspectral Histopathology Image Classification
More concretely, we propose to learn a set of linear coefficients that can be used to represent one band by the remaining bands via masking out these bands.
Bit Allocation using Optimization
In this paper, we consider the problem of bit allocation in Neural Video Compression (NVC).
HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image Classification
A parallel hierarchy of local and global feature blocks is designed to efficiently extract local features and global representations at various semantic scales, with the flexibility to model at different scales and linear computational complexity relevant to image size.
Multi-Sample Training for Neural Image Compression
This paper considers the problem of lossy neural image compression (NIC).
Multi-scale Attention Network for Single Image Super-Resolution
ConvNets can compete with transformers in high-level tasks by exploiting larger receptive fields.
