Search Results for author:
Found 124 papers, 55 papers with code
CSSL-MHTR: Continual Self-Supervised Learning for Scalable Multi-script Handwritten Text Recognition
In this paper, we explore the potential of continual self-supervised learning to alleviate the catastrophic forgetting problem in handwritten text recognition, as an example of sequence recognition.
MSINet: Twins Contrastive Search of Multi-Scale Interaction for Object ReID
Neural Architecture Search (NAS) has been increasingly appealing to the society of object Re-Identification (ReID), for that task-specific architectures significantly improve the retrieval performance.
Preventing Zero-Shot Transfer Degradation in Continual Learning of Vision-Language Models
To address this challenge, we propose a novel method ZSCL to prevent zero-shot transfer degradation in the continual learning of vision-language models in both feature and parameter space.
DiM: Distilling Dataset into Generative Model
To the best of our knowledge, we are the first to achieve higher accuracy on complex architectures than simple ones, such as 75. 1\% with ResNet-18 and 72. 6\% with ConvNet-3 on ten images per class of CIFAR-10.
InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning
We train the full data in the last few epochs to improve the performance of our method, which further reduces the bias of the total update.
DREAM: Efficient Dataset Distillation by Representative Matching
Although there are various matching objectives, currently the strategy for selecting original images is limited to naive random sampling.
Artificial Intelligence System for Detection and Screening of Cardiac Abnormalities using Electrocardiogram Images
In this study, we present an AI system developed to detect and screen cardiac abnormalities (CAs) from real-world ECG images.
Bioformer: an efficient transformer language model for biomedical text mining
We thoroughly evaluated the performance of Bioformer as well as existing biomedical BERT models including BioBERT and PubMedBERT on 15 benchmark datasets of four different biomedical NLP tasks: named entity recognition, relation extraction, question answering and document classification.
FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecasting
However, use of FT is problematic due to Gibbs phenomenon.
Expanding Small-Scale Datasets with Guided Imagination
The two criteria are verified to be essential for effective dataset expansion: GIF-SD obtains 13. 5\% higher model accuracy on natural image datasets than unguided expansion with SD.
Self adaptive global-local feature enhancement for radiology report generation
Automated radiology report generation aims at automatically generating a detailed description of medical images, which can greatly alleviate the workload of radiologists and provide better medical services to remote areas.
Versatile Diffusion: Text, Images and Variations All in One Diffusion Model
Through our experiments, we demonstrate that VD and its underlying framework have the following merits: a) VD handles all subtasks with competitive quality; b) VD initiates novel extensions and applications such as disentanglement of style and semantic, image-text dual-guided generation, etc.
Dataset Factorization for Condensation
In this paper, we study dataset distillation (DD), from a novel perspective and introduce a \emph{dataset factorization} approach, termed \emph{HaBa}, which is a plug-and-play strategy portable to any existing DD baseline.
Dataset Distillation via Factorization
In this paper, we study \xw{dataset distillation (DD)}, from a novel perspective and introduce a \emph{dataset factorization} approach, termed \emph{HaBa}, which is a plug-and-play strategy portable to any existing DD baseline.
MV-HAN: A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendation
In this paper, we introduce a novel Multi-View Approach with Hybrid Attentive Networks (MV-HAN) for contents retrieval at the matching stage of recommender systems.
Vision-Based Defect Classification and Weight Estimation of Rice Kernels
To compensate for the imbalance of different kernel numbers and classify kernels with multiple flaws accurately, we propose a multi-stage workflow which is able to locate the kernels in the captured image and classify their properties.
Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification
Lifelong object re-identification incrementally learns from a stream of re-identification tasks.
Attention Distillation: self-supervised vision transformer students need more guidance
In experiments on ImageNet-Subset and ImageNet-1K, we show that our method AttnDistill outperforms existing self-supervised knowledge distillation (SSKD) methods and achieves state-of-the-art k-NN accuracy compared with self-supervised learning (SSL) methods learning from scratch (with the ViT-S model).
Uncertainty estimations methods for a deep learning model to aid in clinical decision-making -- a clinician's perspective
Especially for smaller, single institutional datasets, it may be important to evaluate multiple estimations techniques before incorporating a model into clinical practice.
RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registration
More specifically, 3D structures of the whole frame are first represented by our global PPF signatures, from which structural descriptors are learned to help geometric descriptors sense the 3D world beyond local regions.
Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers
For Head and Neck Cancers (HNC) patient management, automatic gross tumor volume (GTV) segmentation and accurate pre-treatment cancer recurrence prediction are of great importance to assist physicians in designing personalized management plans, which have the potential to improve the treatment outcome and quality of life for HNC patients.
Deep Lossy Plus Residual Coding for Lossless and Near-lossless Image Compression
In the lossless mode, the DLPR coding system first performs lossy compression and then lossless coding of residuals.
Prompt Vision Transformer for Domain Generalization
In this paper, we propose a novel approach DoPrompt based on prompt learning to embed the knowledge of source domains in domain prompts for target domain prediction.
QuickSkill: Novice Skill Estimation in Online Multiplayer Games
However, the skill rating of a novice is usually inaccurate, as current matchmaking rating algorithms require considerable amount of games for learning the true skill of a new player.
Point-to-Box Network for Accurate Object Detection via Single Point Supervision
However, the performance gap between point supervised object detection (PSOD) and bounding box supervised detection remains large.
OneRing: A Simple Method for Source-free Open-partial Domain Adaptation
In this paper, we investigate Source-free Open-partial Domain Adaptation (SF-OPDA), which addresses the situation where there exist both domain and category shifts between source and target domains.
Progressive Multi-scale Consistent Network for Multi-class Fundus Lesion Segmentation
The two proposed PFF and DAB blocks can be integrated with the off-the-shelf backbone networks to address the two issues of multi-scale and feature inconsistency in the multi-class segmentation of fundus lesions, which will produce better feature representation in the feature space.
Skeleton-based Action Recognition via Temporal-Channel Aggregation
Skeleton-based action recognition methods are limited by the semantic extraction of spatio-temporal skeletal maps.
Ranked #4 on
Skeleton Based Action Recognition
on N-UCLA
Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors
Domain Adaptation of Black-box Predictors (DABP) aims to learn a model on an unlabeled target domain supervised by a black-box predictor trained on a source domain.
FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders
Firstly, randomly masked face images are used to train the reconstruction module in FaceMAE.
Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation
Treating SFDA as an unsupervised clustering problem and following the intuition that local neighbors in feature space should have more similar predictions than other features, we propose to optimize an objective of prediction consistency.
A Novel Speech-Driven Lip-Sync Model with CNN and LSTM
Generating synchronized and natural lip movement with speech is one of the most important tasks in creating realistic virtual characters.
Reliable Label Correction is a Good Booster When Learning with Extremely Noisy Labels
This paradigm, however, is prone to significant degeneration under heavy label noise, as the number of clean samples is too small for conventional methods to behave well.
Grasping the Arrow of Time from the Singularity: Decoding Micromotion in Low-dimensional Latent Spaces from StyleGAN
To study the motion features in the latent space of StyleGAN, in this paper, we hypothesize and demonstrate that a series of meaningful, natural, and versatile small, local movements (referred to as "micromotion", such as expression, head movement, and aging effect) can be represented in low-rank spaces extracted from the latent space of a conventionally pre-trained StyleGAN-v2 model for face generation, with the guidance of proper "anchors" in the form of either short text or video clips.
Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering
While conventional CF models are known for facing the challenges of the popularity bias that favors popular items, one may wonder "Whether the existing graph-based CF models alleviate or exacerbate popularity bias of recommender systems?"
Smoothed Online Combinatorial Optimization Using Imperfect Predictions
Smoothed online combinatorial optimization considers a learner who repeatedly chooses a combinatorial decision to minimize an unknown changing cost function with a penalty on switching decisions in consecutive rounds.
Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations
To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature.
Multi-label topic classification for COVID-19 literature with Bioformer
Our results show that Bioformer outperforms BioBERT and PubMedBERT in this task.
Modeling Motion with Multi-Modal Features for Text-Based Video Segmentation
Text-based video segmentation aims to segment the target object in a video based on a describing sentence.
Ranked #7 on
Referring Expression Segmentation
on A2D Sentences
Optical Flow Estimation
Referring Expression Segmentation
+2
Deep Q-learning of global optimizer of multiply model parameters for viscoelastic imaging
Significance: DQMP method is promising for imaging of multiple parameters, and can be generalized to global optimization for many other complex nonconvex functions and imaging of physical parameters.
Decision-Focused Learning without Differentiable Optimization: Learning Locally Optimized Decision Losses
Decision-Focused Learning (DFL) is a paradigm for tailoring a predictive model to a downstream optimization task that uses its predictions in order to perform better on that specific task.
MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning
Prior works either simply align the global features of an image with its associated class semantic vector or utilize unidirectional attention to learn the limited latent semantic representations, which could not effectively discover the intrinsic semantic knowledge e. g., attribute semantics) between visual and attribute features.
CAFE: Learning to Condense Dataset by Aligning Features
Dataset condensation aims at reducing the network training effort through condensing a cumbersome training set into a compact synthetic one.
Improving Non-native Word-level Pronunciation Scoring with Phone-level Mixup Data Augmentation and Multi-source Information
Moreover, we utilize multi-source information (e. g., MFCC and deep features) to further improve the scoring system performance.
Crafting Better Contrastive Views for Siamese Representation Learning
For high performance Siamese representation learning, one of the keys is to design good contrastive pairs.
Scalable Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Health
To address this shortcoming, we propose a novel approach for decision-focused learning in RMAB that directly trains the predictive model to maximize the Whittle index solution quality.
Swift and Sure: Hardness-aware Contrastive Learning for Low-dimensional Knowledge Graph Embeddings
Knowledge graph embedding (KGE) has shown great potential in automatic knowledge graph (KG) completion and knowledge-driven tasks.
Roominoes: Generating Novel 3D Floor Plans From Existing 3D Rooms
We then discuss different strategies for solving the problem, and design two representative pipelines: one uses available 2D floor plans to guide selection and deformation of 3D rooms; the other learns to retrieve a set of compatible 3D rooms and combine them into novel layouts.
The Shape Part Slot Machine: Contact-based Reasoning for Generating 3D Shapes from Parts
We present the Shape Part Slot Machine, a new method for assembling novel 3D shapes from existing parts by performing contact-based reasoning.
Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition
We propose an approach to IML, which we call Episodic Replay Distillation (ERD), that mixes classes from the current task with class exemplars from previous tasks when sampling episodes for meta-learning.
Deciphering the Language of Nature: A transformer-based language model for deleterious mutations in proteins
In this study, we introduce MutFormer, a transformer-based model for the prediction of deleterious missense mutations, which uses reference and mutated protein sequences from the human genome as the primary features.
HCV: Hierarchy-Consistency Verification for Incremental Implicitly-Refined Classification
To overcome forgetting in this benchmark, we propose Hierarchy-Consistency Verification (HCV) as an enhancement to existing continual learning methods.
RL4RS: A Real-World Benchmark for Reinforcement Learning based Recommender System
Reinforcement learning based recommender systems (RL-based RS) aim at learning a good policy from a batch of collected data, by casting sequential recommendations to multi-step decision-making tasks.
Feudal Reinforcement Learning by Reading Manuals
The manager agent is a multi-hop plan generator dealing with high-level abstract information and generating a series of sub-goals in a backward manner.
Eden: A Unified Environment Framework for Booming Reinforcement Learning Algorithms
In this framework, the environment can be easily configured to realize all kinds of RL tasks in the mainstream research.
Harnessing Heterogeneity: Learning from Decomposed Feedback in Bayesian Modeling
We introduce a novel decomposed GP regression to incorporate the subgroup decomposed feedback.
Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning
In the predict-then-optimize framework, the objective is to train a predictive model, mapping from environment features to parameters of an optimization problem, which maximizes decision quality when the optimization is subsequently solved.
Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning
In the predict-then-optimize framework, the objective is to train a predictive model, mapping from environment features to parameters of an optimization problem, which maximizes decision quality when the optimization is subsequently solved.
An Efficient Training Approach for Very Large Scale Face Recognition
This method adopts Dynamic Class Pool (DCP) for storing and updating the identities features dynamically, which could be regarded as a substitute for the FC layer.
Ranked #1 on
Face Verification
on IJB-C
(training dataset metric)
ACAE-REMIND for Online Continual Learning with Compressed Feature Replay
Methods are typically allowed to use a limited buffer to store some of the images in the stream.
Learning to Cluster Faces via Transformer
In this paper, we repurpose the well-known Transformer and introduce a Face Transformer for supervised face clustering.
Continual learning in cross-modal retrieval
We found that the indexing stage pays an important role and that simply avoiding reindexing the database with updated embedding networks can lead to significant gains.
Personalized Bundle Recommendation in Online Games
In business domains, \textit{bundling} is one of the most important marketing strategies to conduct product promotions, which is commonly used in online e-commerce and offline retailers.
Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation
As a part of the value function, free from the sparse and high-variance reward signals, a high-capacity reward-independent world model is trained to simulate complex environmental dynamics under a certain goal.
Model-based Reinforcement Learning
Recommendation Systems
+2
Hyperbolic Geometry is Not Necessary: Lightweight Euclidean-Based Models for Low-Dimensional Knowledge Graph Embeddings
Recent knowledge graph embedding (KGE) models based on hyperbolic geometry have shown great potential in a low-dimensional embedding space.
TSTNN: Two-stage Transformer based Neural Network for Speech Enhancement in the Time Domain
In this paper, we propose a transformer-based architecture, called two-stage transformer neural network (TSTNN) for end-to-end speech denoising in the time domain.
On Implicit Attribute Localization for Generalized Zero-Shot Learning
Zero-shot learning (ZSL) aims to discriminate images from unseen classes by exploiting relations to seen classes via their attribute-based descriptions.
A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection
In ophthalmology, early fundus screening is an economic and effective way to prevent blindness caused by ophthalmic diseases.
Applications of Deep Learning in Fundus Images: A Review
The use of fundus images for the early screening of eye diseases is of great clinical importance.
Exploring Emotion Features and Fusion Strategies for Audio-Video Emotion Recognition
The audio-video based emotion recognition aims to classify a given video into basic emotions.
Facial Expression Recognition (FER)
Video Emotion Recognition
AdnFM: An Attentive DenseNet based Factorization Machine for CTR Prediction
In this paper, we consider the Click-Through-Rate (CTR) prediction problem.
AU-Guided Unsupervised Domain Adaptive Facial Expression Recognition
The domain diversities including inconsistent annotation and varied image collection conditions inevitably exist among different facial expression recognition (FER) datasets, which pose an evident challenge for adapting the FER model trained on one dataset to another one.
Takagi topological insulator with odd $\mathcal P\mathcal T$ pairs of corner states
Particularly, the global Takagi's factorization can (cannot) be done on a $3$D ($2$D) sphere.
Mesoscale and Nanoscale Physics
Interpretable Visual Reasoning via Induced Symbolic Space
Finally, we achieve a higher level of interpretability by imposing OCCAM on the objects represented in the induced symbolic concept space.
Ranked #3 on
Visual Question Answering
on CLEVR
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning
In this paper, we provide a theory of using graph neural networks (GNNs) for multi-node representation learning (where we are interested in learning a representation for a set of more than one node, such as link).
Ranked #1 on
Link Property Prediction
on ogbl-citation2
Suppressing Mislabeled Data via Grouping and Self-Attention
Specifically, this plug-and-play AFM first leverages a \textit{group-to-attend} module to construct groups and assign attention weights for group-wise samples, and then uses a \textit{mixup} module with the attention weights to interpolate massive noisy-suppressed samples.
MulDE: Multi-teacher Knowledge Distillation for Low-dimensional Knowledge Graph Embeddings
Link prediction based on knowledge graph embeddings (KGE) aims to predict new triples to automatically construct knowledge graphs (KGs).
A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis
In this review, we present a comprehensive overview of pathology image analysis based on the markov random fields (MRFs) and conditional random fields (CRFs), which are two popular random field models.
Revisiting Graph Neural Networks for Link Prediction
Graph neural networks (GNNs) have achieved great success in recent years.
ShapeAssembly: Learning to Generate Programs for 3D Shape Structure Synthesis
The program captures the subset of variability that is interpretable and editable.
Dual-Mandate Patrols: Multi-Armed Bandits for Green Security
Conservation efforts in green security domains to protect wildlife and forests are constrained by the limited availability of defenders (i. e., patrollers), who must patrol vast areas to protect from attackers (e. g., poachers or illegal loggers).
Measuring galaxy abundance and clustering at high redshift from incomplete spectroscopic data: Tests on mock catalogs and application to zCOSMOS
Using realistic mock catalogs we show that target selection and redshift incompleteness can lead to significantly biased results.
Astrophysics of Galaxies
CardioLearn: A Cloud Deep Learning Service for Cardiac Disease Detection from Electrocardiogram
Electrocardiogram (ECG) is one of the most convenient and non-invasive tools for monitoring peoples' heart condition, which can use for diagnosing a wide range of heart diseases, including Cardiac Arrhythmia, Acute Coronary Syndrome, et al.
Low-Resource Generation of Multi-hop Reasoning Questions
Specifically, we first build a multi-hop generation model and guide it to satisfy the logical rationality by the reasoning chain extracted from a given text.
Bookworm continual learning: beyond zero-shot learning and continual learning
We propose bookworm continual learning(BCL), a flexible setting where unseen classes can be inferred via a semantic model, and the visual model can be updated continually.
Automatically Learning Compact Quality-aware Surrogates for Optimization Problems
Solving optimization problems with unknown parameters often requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using these values.
Simple and effective localized attribute representations for zero-shot learning
Zero-shot learning (ZSL) aims to discriminate images from unseen classes by exploiting relations to seen classes via their semantic descriptions.
Relational Graph Attention Network for Aspect-based Sentiment Analysis
Then, we propose a relational graph attention network (R-GAT) to encode the new tree structure for sentiment prediction.
Multi-Domain Dialogue Acts and Response Co-Generation
Unlike those pipeline approaches, our act generation module preserves the semantic structures of multi-domain dialogue acts and our response generation module dynamically attends to different acts as needed.
The 1st Agriculture-Vision Challenge: Methods and Results
The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset.
Semantic Drift Compensation for Class-Incremental Learning
The vast majority of methods have studied this scenario for classification networks, where for each new task the classification layer of the network must be augmented with additional weights to make room for the newly added classes.
Axis Learning for Orientated Objects Detection in Aerial Images
Arbitrary orientated objects are detected by predicting the axis of the object, which is the line connecting the head and tail of the object, and the width of the object is vertical to the axis.
Ranked #1 on
One-stage Anchor-free Oriented Object Detection
on HRSC2016
( mAP metric)
Relating the structure of dark matter halos to their assembly and environment
Using decision trees built with the random ensemble method, we find that the correlation between halo concentration and assembly history is tight: more than 60% of the variance in halo concentration can be explained by assembly history alone.
Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics
Suppressing Uncertainties for Large-Scale Facial Expression Recognition
Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, and the subjectiveness of annotators.
PA-Cache: Evolving Learning-Based Popularity-Aware Content Caching in Edge Networks
Compared to the conventional content delivery networks, caches in edge networks with smaller sizes usually have to accommodate more bursty requests.
Aesthetic Quality Assessment for Group photograph
Image aesthetic quality assessment has got much attention in recent years, but not many works have been done on a specific genre of photos: Group photograph.
A Realistic Face-to-Face Conversation System based on Deep Neural Networks
Based on the models' output, the synthesizer uses the Pixel2Pixel model to generate realistic facial images.
CBOWRA: A Representation Learning Approach for Medication Anomaly Detection
Electronic health record is an important source for clinical researches and applications, and errors inevitably occur in the data, which could lead to severe damages to both patients and hospital services.
Video synthesis of human upper body with realistic face
This paper presents a generative adversarial learning-based human upper body video synthesis approach to generate an upper body video of target person that is consistent with the body motion, face expression, and pose of the person in source video.
Deep Learning based Wearable Assistive System for Visually Impaired People
In this paper, we propose a deep learning based assistive system to improve the environment perception experience of visually impaired (VI).
Coupled-Projection Residual Network for MRI Super-Resolution
The deep sub-network learns from the residuals of the high-frequency image information, where multiple residual blocks are cascaded to magnify the MRI images at the last network layer.
Bootstrap Model Ensemble and Rank Loss for Engagement Intensity Regression
Based on our winner solution last year, we mainly explore head features and body features with a bootstrap strategy and two novel loss functions in this paper.
Frame attention networks for facial expression recognition in videos
The feature embedding module is a deep Convolutional Neural Network (CNN) which embeds face images into feature vectors.
Ranked #2 on
Facial Expression Recognition (FER)
on CK+
(Accuracy (7 emotion) metric)
BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization
The success of neural summarization models stems from the meticulous encodings of source articles.
Ranked #14 on
Text Summarization
on GigaWord
Vision-based Robotic Grasping From Object Localization, Object Pose Estimation to Grasp Estimation for Parallel Grippers: A Review
We conclude three key tasks during vision-based robotic grasping, which are object localization, object pose estimation and grasp estimation.
LSANet: Feature Learning on Point Sets by Local Spatial Aware Layer
We design a novel Local Spatial Aware (LSA) layer, which can learn to generate Spatial Distribution Weights (SDWs) hierarchically based on the spatial relationship in local region for spatial independent operations, to establish the relationship between these operations and spatial distribution, thus capturing the local geometric structure sensitively. We further propose the LSANet, which is based on LSA layer, aggregating the spatial information with associated features in each layer of the network better in network design. The experiments show that our LSANet can achieve on par or better performance than the state-of-the-art methods when evaluating on the challenging benchmark datasets.
Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition
Extensive experiments show that our RAN and region biased loss largely improve the performance of FER with occlusion and variant pose.
Ranked #1 on
Facial Expression Recognition (FER)
on SFEW
Towards More Realistic Human-Robot Conversation: A Seq2Seq-based Body Gesture Interaction System
This paper presents a novel system that enables intelligent robots to exhibit realistic body gestures while communicating with humans.
Deep Learning Based Robot for Automatically Picking up Garbage on the Grass
In addition, with the ground segmentation using a deep neural network, a novel navigation strategy is proposed to guide the robot to move around.
Virtual-Blind-Road Following Based Wearable Navigation Device for Blind People
To help the blind people walk to the destination efficiently and safely in indoor environment, a novel wearable navigation device is presented in this paper.
Synthetic Data Generation and Adaption for Object Detection in Smart Vending Machines
This paper presents an improved scheme for the generation and adaption of synthetic images for the training of deep Convolutional Neural Networks(CNNs) to perform the object detection task in smart vending machines.
FluidC+: A novel community detection algorithm based on fluid propagation
In recent years, community detection has gradually become a hot topic in the complex network data mining field.
A Survey on Face Data Augmentation
The quality and size of training set have great impact on the results of deep learning-based face related tasks.
Detecting Colorized Images via Convolutional Neural Networks: Toward High Accuracy and Good Generalization
First, we design and implement a base network, which can attain better performance in terms of classification accuracy and generalization (in most cases) compared with state-of-the-art methods.
A Unified Framework for Mutual Improvement of SLAM and Semantic Segmentation
This paper presents a novel framework for simultaneously implementing localization and segmentation, which are two of the most important vision-based tasks for robotics.
Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models
We present a new, fast and flexible pipeline for indoor scene synthesis that is based on deep convolutional generative models.
On the Generation of Medical Question-Answer Pairs
Question answering (QA) has achieved promising progress recently.
Sub-GAN: An Unsupervised Generative Model via Subspaces
The recent years have witnessed significant growth in constructing robust generative models to capture informative distributions of natural data.
LIUM-CVC Submissions for WMT18 Multimodal Translation Task
This paper describes the multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT18 Shared Task on Multimodal Translation.
Knowledge Graph Embedding with Entity Neighbors and Deep Memory Network
Knowledge Graph Embedding (KGE) aims to represent entities and relations of knowledge graph in a low-dimensional continuous vector space.
Deep Learning Analysis of Defect and Phase Evolution During Electron Beam Induced Transformations in WS2
Understanding elementary mechanisms behind solid-state phase transformations and reactions is the key to optimizing desired functional properties of many technologically relevant materials.
Materials Science
BTS-DSN: Deeply Supervised Neural Network with Short Connections for Retinal Vessel Segmentation
Results: The proposed BTS-DSN has been verified on DRIVE, STARE and CHASE_DB1 datasets, and showed competitive performance over other state-of-the-art methods.
Richer Convolutional Features for Edge Detection
Using VGG16 network, we achieve \sArt results on several available datasets.
Ranked #5 on
Edge Detection
on BIPED
