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Found 149 papers, 53 papers with code
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
In this report, we present the latest model of the Gemini family, Gemini 1. 5 Pro, a highly compute-efficient multimodal mixture-of-experts model capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio.
Ranked #6 on
Code Generation
on HumanEval
An invariance constrained deep learning network for PDE discovery
However, the discovery of governing equations from sparse data with high noise is still very challenging due to the difficulty of derivatives computation and the disturbance of noise.
BusReF: Infrared-Visible images registration and fusion focus on reconstructible area using one set of features
We focus on Infrared-Visible image registration and fusion task (IVRF).
VOT: Revolutionizing Speaker Verification with Memory and Attention Mechanisms
Speaker verification is to judge the similarity of two unknown voices in an open set, where the ideal speaker embedding should be able to condense discriminant information into a compact utterance-level representation that has small intra-speaker distances and large inter-speaker distances. We propose a novel model named Voice Transformer(VOT) for speaker verification.
TextFusion: Unveiling the Power of Textual Semantics for Controllable Image Fusion
Advanced image fusion methods are devoted to generating the fusion results by aggregating the complementary information conveyed by the source images.
Gemini: A Family of Highly Capable Multimodal Models
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding.
Ranked #1 on
Multi-task Language Understanding
on MMLU
(using extra training data)
MMICT: Boosting Multi-Modal Fine-Tuning with In-Context Examples
Although In-Context Learning (ICL) brings remarkable performance gains to Large Language Models (LLMs), the improvements remain lower than fine-tuning on downstream tasks.
Multi-dimensional Fair Federated Learning
Federated learning (FL) has emerged as a promising collaborative and secure paradigm for training a model from decentralized data without compromising privacy.
Linker-Tuning: Optimizing Continuous Prompts for Heterodimeric Protein Prediction
Predicting the structure of interacting chains is crucial for understanding biological systems and developing new drugs.
Riemannian Self-Attention Mechanism for SPD Networks
Symmetric positive definite (SPD) matrix has been demonstrated to be an effective feature descriptor in many scientific areas, as it can encode spatiotemporal statistics of the data adequately on a curved Riemannian manifold, i. e., SPD manifold.
Code Search Debiasing:Improve Search Results beyond Overall Ranking Performance
To mitigate biases, we develop a general debiasing framework that employs reranking to calibrate search results.
FlashOcc: Fast and Memory-Efficient Occupancy Prediction via Channel-to-Height Plugin
We apply the FlashOCC to diverse occupancy prediction baselines on the challenging Occ3D-nuScenes benchmarks and conduct extensive experiments to validate the effectiveness.
Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching
The ultimate goal of Dataset Distillation is to synthesize a small synthetic dataset such that a model trained on this synthetic set will perform equally well as a model trained on the full, real dataset.
ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility
The automated assembly of complex products requires a system that can automatically plan a physically feasible sequence of actions for assembling many parts together.
MSG-BART: Multi-granularity Scene Graph-Enhanced Encoder-Decoder Language Model for Video-grounded Dialogue Generation
Generating dialogue grounded in videos requires a high level of understanding and reasoning about the visual scenes in the videos.
Innovative Digital Storytelling with AIGC: Exploration and Discussion of Recent Advances
Digital storytelling, as an art form, has struggled with cost-quality balance.
RBFormer: Improve Adversarial Robustness of Transformer by Robust Bias
Recently, there has been a surge of interest and attention in Transformer-based structures, such as Vision Transformer (ViT) and Vision Multilayer Perceptron (VMLP).
MMAPS: End-to-End Multi-Grained Multi-Modal Attribute-Aware Product Summarization
Furthermore, we model product attributes based on both text and image modalities so that multi-modal product characteristics can be manifested in the generated summaries.
Single-User Injection for Invisible Shilling Attack against Recommender Systems
In shilling attack, an adversarial party injects a number of well-designed fake user profiles into the system to mislead RS so that the attack goal can be achieved.
Knowledge Prompt-tuning for Sequential Recommendation
Pre-trained language models (PLMs) have demonstrated strong performance in sequential recommendation (SR), which are utilized to extract general knowledge.
DeRisk: An Effective Deep Learning Framework for Credit Risk Prediction over Real-World Financial Data
Despite the tremendous advances achieved over the past years by deep learning techniques, the latest risk prediction models for industrial applications still rely on highly handtuned stage-wised statistical learning tools, such as gradient boosting and random forest methods.
Crowdsourcing Fraud Detection over Heterogeneous Temporal MMMA Graph
The rise of the click farm business using Multi-purpose Messaging Mobile Apps (MMMAs) tempts cybercriminals to perpetrate crowdsourcing frauds that cause financial losses to click farm workers.
Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities
Compared with typical ST data (e. g., traffic data), ST ocean data is more complicated but with unique characteristics, e. g., diverse regionality and high sparsity.
Scale-Rotation-Equivariant Lie Group Convolution Neural Networks (Lie Group-CNNs)
In addition, the generalization ability of the Lie group-CNN on SIM(2) on rotation-equivariance is verified on rotated-MNIST and rotated-CIFAR10, and the robustness of the network is verified on SO(2) and SE(2).
DePF: A Novel Fusion Approach based on Decomposition Pooling for Infrared and Visible Images
Infrared and visible image fusion aims to generate synthetic images simultaneously containing salient features and rich texture details, which can be used to boost downstream tasks.
LE2Fusion: A novel local edge enhancement module for infrared and visible image fusion
Specifically, a local edge enhancement (LE2) module is proposed to improve the edge information under complex illumination conditions and preserve the essential features of image.
Technical outlier detection via convolutional variational autoencoder for the ADMANI breast mammogram dataset
The ADMANI datasets (annotated digital mammograms and associated non-image datasets) from the Transforming Breast Cancer Screening with AI programme (BRAIx) run by BreastScreen Victoria in Australia are multi-centre, large scale, clinically curated, real-world databases.
FusionBooster: A Unified Image Fusion Boosting Paradigm
We argue that there is a scope to improve the fusion performance with the help of the FusionBooster, a model specifically designed for the fusion task.
Class Attention Transfer Based Knowledge Distillation
Previous knowledge distillation methods have shown their impressive performance on model compression tasks, however, it is hard to explain how the knowledge they transferred helps to improve the performance of the student network.
UHRNet: A Deep Learning-Based Method for Accurate 3D Reconstruction from a Single Fringe-Pattern
Our experimental results show that our proposed method can increase the accuracy of 3D reconstruction from a single fringe pattern.
LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Images
In particular we adopt a learnable representation approach to the fusion task, in which the construction of the fusion network architecture is guided by the optimisation algorithm producing the learnable model.
SMUG: Towards robust MRI reconstruction by smoothed unrolling
To address this problem, we propose a novel image reconstruction framework, termed SMOOTHED UNROLLING (SMUG), which advances a deep unrolling-based MRI reconstruction model using a randomized smoothing (RS)-based robust learning operation.
Scale-aware Two-stage High Dynamic Range Imaging
In the first stage of feature fusion, we obtain a preliminary fusion result with little ghosting.
Optical Flow Estimation
Vocal Bursts Intensity Prediction
+1
Practical Cross-System Shilling Attacks with Limited Access to Data
With the idea of Cross-system Attack, we design a Practical Cross-system Shilling Attack (PC-Attack) framework that requires little information about the victim RS model and the target RS data for conducting attacks.
Weight-guided class complementing for long-tailed image recognition
In this paper, we propose a Weight-Guided Class Complementing framework to address this issue.
Ranked #11 on
Long-tail Learning
on CIFAR-10-LT (ρ=100)
Image Super-Resolution using Efficient Striped Window Transformer
However, the challenge of balancing model performance and complexity has hindered their application in lightweight SR (LSR).
In-situ monitoring additive manufacturing process with AI edge computing
Inference time of ViTSR and FCN was optimized to 50. 97 ms and 67. 86 ms on AI edge board after operator fusion and model pruning.
CORE: Co-planarity Regularized Monocular Geometry Estimation with Weak Supervision
Meanwhile, SANE easily establishes multi-task learning with CORE loss functions on both depth and surface normal estimation, leading to the whole performance leap.
NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical Development Patterns of Preterm Infants
Deploying reliable deep learning techniques in interdisciplinary applications needs learned models to output accurate and (even more importantly) explainable predictions.
Rethinking Feature-Based Knowledge Distillation for Face Recognition
By constraining the teacher's search space with reverse distillation, we narrow the intrinsic gap and unleash the potential of feature-only distillation.
Galaxy Image Classification using Hierarchical Data Learning with Weighted Sampling and Label Smoothing
With the development of a series of Galaxy sky surveys in recent years, the observations increased rapidly, which makes the research of machine learning methods for galaxy image recognition a hot topic.
Automated Optical Inspection of FAST's Reflector Surface using Drones and Computer Vision
Hence, the timely and accurate detection of surface defects is crucial for FAST's stable operation.
Fully and Weakly Supervised Referring Expression Segmentation with End-to-End Learning
To validate our framework on a weakly-supervised setting, we annotated three RES benchmark datasets (RefCOCO, RefCOCO+ and RefCOCOg) with click annotations. Our method is simple but surprisingly effective, outperforming all previous state-of-the-art RES methods on fully- and weakly-supervised settings by a large margin.
Self-supervised Graph Representation Learning for Black Market Account Detection
This paper illustrates our BMA detection system SGRL (Self-supervised Graph Representation Learning) used in WeChat, a representative MMMA with over a billion users.
xTrimoABFold: De novo Antibody Structure Prediction without MSA
Its improvement in both accuracy and efficiency makes it a valuable tool for de novo antibody design and could make further improvements in immuno-theory.
Towards Accurate Facial Landmark Detection via Cascaded Transformers
We formulate facial landmark detection as a coordinate regression task such that the model can be trained end-to-end.
Ranked #2 on
Face Alignment
on COFW
HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative
Our proposed method, HelixFold-Single, first pre-trains a large-scale protein language model (PLM) with thousands of millions of primary sequences utilizing the self-supervised learning paradigm, which will be used as an alternative to MSAs for learning the co-evolution information.
MLRIP: Pre-training a military language representation model with informative factual knowledge and professional knowledge base
Incorporating prior knowledge into pre-trained language models has proven to be effective for knowledge-driven NLP tasks, such as entity typing and relation extraction.
A Medical Image Fusion Method based on MDLatLRRv2
Since MDLatLRR only considers detailed parts (salient features) of input images extracted by latent low-rank representation (LatLRR), it doesn't use base parts (principal features) extracted by LatLRR effectively.
Shilling Black-box Recommender Systems by Learning to Generate Fake User Profiles
Leg-UP learns user behavior patterns from real users in the sampled ``templates'' and constructs fake user profiles.
FD-CAM: Improving Faithfulness and Discriminability of Visual Explanation for CNNs
In this paper, we propose a novel CAM weighting scheme, named FD-CAM, to improve both the faithfulness and discriminability of the CAM-based CNN visual explanation.
Learning Dense Reward with Temporal Variant Self-Supervision
Rewards play an essential role in reinforcement learning.
Visual Encoding and Debiasing for CTR Prediction
In this paper, we present a visual encoding framework for CTR prediction to overcome these problems.
SwinFuse: A Residual Swin Transformer Fusion Network for Infrared and Visible Images
The existing deep learning fusion methods mainly concentrate on the convolutional neural networks, and few attempts are made with transformer.
Pushing the Performance Limit of Scene Text Recognizer without Human Annotation
A robust consistency regularization based semi-supervised framework is proposed for STR, which can effectively solve the instability issue due to domain inconsistency between synthetic and real images.
Discovering Governing Equations by Machine Learning implemented with Invariance
The partial differential equation (PDE) plays a significantly important role in many fields of science and engineering.
FCNet: A Convolutional Neural Network for Arbitrary-Length Exposure Estimation
For image exposure enhancement, the tasks of Single-Exposure Correction (SEC) and Multi-Exposure Fusion (MEF) are widely studied in the image processing community.
Content-Noise Complementary Learning for Medical Image Denoising
In this study, we propose a simple yet effective strategy, the content-noise complementary learning (CNCL) strategy, in which two deep learning predictors are used to learn the respective content and noise of the image dataset complementarily.
Exploring Fusion Strategies for Accurate RGBT Visual Object Tracking
The effectiveness of the proposed decision-level fusion strategy owes to a number of innovative contributions, including a dynamic weighting of the RGB and TIR contributions and a linear template update operation.
Res2NetFuse: A Fusion Method for Infrared and Visible Images
The Res2Net-based encoder is used to extract multi-scale features of source images, the paper introducing a new training strategy for training a Res2Net-based encoder that uses only a single image.
Enhanced countering adversarial attacks via input denoising and feature restoring
Despite the fact that deep neural networks (DNNs) have achieved prominent performance in various applications, it is well known that DNNs are vulnerable to adversarial examples/samples (AEs) with imperceptible perturbations in clean/original samples.
A novel multiobjective evolutionary algorithm based on decomposition and multi-reference points strategy
For performance assessment, the proposed algorithm is compared with existing four state-of-the-art multiobjective evolutionary algorithms on benchmark test problems with various types of Pareto optimal fronts.
A Novel Solution for Uu Interface Based C-V2X
C-V2X includes two solutions: Uu interface based C-V2X and PC5 interface based C-V2X.
Unsupervised Local Discrimination for Medical Images
To address this challenge, in this paper, we propose a general unsupervised representation learning framework, named local discrimination (LD), to learn local discriminative features for medical images by closely embedding semantically similar pixels and identifying regions of similar structures across different images.
Attention-Guided Progressive Neural Texture Fusion for High Dynamic Range Image Restoration
A progressive texture blending module is designed to blend the encoded two-stream features in a multi-scale and progressive manner.
SHORING: Design Provable Conditional High-Order Interaction Network via Symbolic Testing
Deep learning provides a promising way to extract effective representations from raw data in an end-to-end fashion and has proven its effectiveness in various domains such as computer vision, natural language processing, etc.
MatchVIE: Exploiting Match Relevancy between Entities for Visual Information Extraction
Through key-value matching based on relevancy evaluation, the proposed MatchVIE can bypass the recognitions to various semantics, and simply focuses on the strong relevancy between entities.
TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning
Secondly, on top of the proposed graph transformer, we introduce a two-stream encoder that separately extracts representations from temporal neighborhoods associated with the two interaction nodes and then utilizes a co-attentional transformer to model inter-dependencies at a semantic level.
Towards an efficient framework for Data Extraction from Chart Images
For building a robust point detector, a fully convolutional network with feature fusion module is adopted, which can distinguish close points compared to traditional methods.
Non-Homogeneous Haze Removal via Artificial Scene Prior and Bidimensional Graph Reasoning
In this paper, we propose a Non-Homogeneous Haze Removal Network (NHRN) via artificial scene prior and bidimensional graph reasoning.
Code Completion by Modeling Flattened Abstract Syntax Trees as Graphs
Code completion has become an essential component of integrated development environments.
Improving Code Summarization with Block-wise Abstract Syntax Tree Splitting
In this paper, we present the Block-wise Abstract Syntax Tree Splitting method (BASTS for short), which fully utilizes the rich tree-form syntax structure in ASTs, for improving code summarization.
RFN-Nest: An end-to-end residual fusion network for infrared and visible images
The most difficult part of the design is to choose an appropriate strategy to generate the fused image for a specific task in hand.
A Bioinspired Retinal Neural Network for Accurately Extracting Small-Target Motion Information in Cluttered Backgrounds
Robust and accurate detection of small moving targets in cluttered moving backgrounds is a significant and challenging problem for robotic visual systems to perform search and tracking tasks.
A Bioinspired Approach-Sensitive Neural Network for Collision Detection in Cluttered and Dynamic Backgrounds
Rapid, accurate and robust detection of looming objects in cluttered moving backgrounds is a significant and challenging problem for robotic visual systems to perform collision detection and avoidance tasks.
CelebA-Spoof Challenge 2020 on Face Anti-Spoofing: Methods and Results
It is the largest face anti-spoofing dataset in terms of the numbers of the data and the subjects.
Sequential Recommendation in Online Games with Multiple Sequences, Tasks and User Levels
We leverage Graph Neural Network and multi-task learning to design M$^3$Rec in order to model the complex information in the heterogeneous sequential recommendation scenario of Tencent Games.
Self Sparse Generative Adversarial Networks
In this work, we propose a Self Sparse Generative Adversarial Network (Self-Sparse GAN) that reduces the parameter space and alleviates the zero gradient problem.
Homogeneous superconducting gap in DBCO synthesized by oxide molecular beam epitaxy
This indicates that the pseudogap and superconductivity are of different origins.
Superconductivity Materials Science
Improving Learning to Branch via Reinforcement Learning
Branch-and-Bound~(B\&B) is a general and widely used algorithm paradigm for solving Mixed Integer Programming~(MIP).
To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices
Recent advances in machine learning, wireless communication, and mobile hardware technologies promisingly enable federated learning (FL) over massive mobile edge devices, which opens new horizons for numerous intelligent mobile applications.
Monitoring-based Differential Privacy Mechanism Against Query-Flooding Parameter Duplication Attack
In MDP, we first propose a novel real-time model extraction status assessment scheme called Monitor to evaluate the situation of the model.
Where to Look and How to Describe: Fashion Image Retrieval with an Attentional Heterogeneous Bilinear Network
Fashion products typically feature in compositions of a variety of styles at different clothing parts.
Deep Learning is Singular, and That's Good
In singular models, the optimal set of parameters forms an analytic set with singularities and classical statistical inference cannot be applied to such models.
A Learning Approach to Robot-Agnostic Force-Guided High Precision Assembly
In this work we propose a learning approach to high-precision robotic assembly problems.
Transportation Internet: Concepts, Models, and Architectures
Disruptive changes in vehicles and transportation have been triggered by automated, connected, electrified and shared mobility.
Fast and Secure Distributed Nonnegative Matrix Factorization
Nonnegative matrix factorization (NMF) has been successfully applied in several data mining tasks.
Single Cell Transcriptome Research in Human Placenta
Human placenta is a complex and heterogeneous organ interfacing between the mother and the fetus that supports fetal development.
NestFuse: An Infrared and Visible Image Fusion Architecture based on Nest Connection and Spatial/Channel Attention Models
In our proposed fusion strategy, spatial attention models and channel attention models are developed that describe the importance of each spatial position and of each channel with deep features.
Outlier Detection Using a Novel method: Quantum Clustering
This approach, called Quantum Clustering (QC), deals with unlabeled data processing and constructs a potential function to find the centroids of clusters and the outliers.
A Robust Attentional Framework for License Plate Recognition in the Wild
On the other hand, the 2D attentional based license plate recognizer with an Xception-based CNN encoder is capable of recognizing license plates with different patterns under various scenarios accurately and robustly.
Give Me Something to Eat: Referring Expression Comprehension with Commonsense Knowledge
In this case, we need to use commonsense knowledge to identify the objects in the image.
Structured Multimodal Attentions for TextVQA
In this paper, we propose an end-to-end structured multimodal attention (SMA) neural network to mainly solve the first two issues above.
Quantifying socioeconomic activities and weather effects on the global spread of COVID-19 epidemic
We finally estimate the reduced reproductive number and the population spared from infections due to restricting SA at 40, 964, 180, 336, 174, 494, in China, United States, and Europe respectively.
Attacking Recommender Systems with Augmented User Profiles
Recommendation Systems (RS) have become an essential part of many online services.
Variational Policy Propagation for Multi-agent Reinforcement Learning
We propose a \emph{collaborative} multi-agent reinforcement learning algorithm named variational policy propagation (VPP) to learn a \emph{joint} policy through the interactions over agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Underwater image enhancement with Image Colorfulness Measure
To improve the visual quality of underwater images, we proposed a novel enhancement model, which is a trainable end-to-end neural model.
Machine-learning-based methods for output only structural modal identification
A self-coding deep neural network is designed to identify the structural modal parameters from the vibration data of structures.
Recovering compressed images for automatic crack segmentation using generative models
We develop a recovery framework for automatic crack segmentation of compressed crack images based on this new CS method and demonstrate the remarkable performance of the method taking advantage of the strong capability of generative models to capture the necessary features required in the crack segmentation task even the backgrounds of the generated images are not well reconstructed.
Magnitude and significance of the peak of early embryonic mortality
We find that, in line with the few other species for which data are available, the embryonic mortality of zebrafish has a prominent peak shortly after fertilization.
Dynamic Experience Replay
Our ablation studies show that Dynamic Experience Replay is a crucial ingredient that either largely shortens the training time in these challenging environments or solves the tasks that the vanilla Ape-X DDPG cannot solve.
Publicly Verifiable Databases With All Efficient Updating Operations
As a result, it remains an open problem how to construct an efficient (and publicly verifiable) VDB scheme that can support all updating operations regardless of the manner of insertion.
Mitigating Query-Flooding Parameter Duplication Attack on Regression Models with High-Dimensional Gaussian Mechanism
Public intelligent services enabled by machine learning algorithms are vulnerable to model extraction attacks that can steal confidential information of the learning models through public queries.
Improved dual channel pulse coupled neural network and its application to multi-focus image fusion
This paper presents an improved dual channel pulse coupled neural network (IDC-PCNN) model for image fusion.
Multi-focus Image Fusion Based on Similarity Characteristics
For three source images, a joint region segmentation method based on segmentation of two images is used to obtain the final segmentation result.
Progressive Sample Mining and Representation Learning for One-Shot Person Re-identification with Adversarial Samples
To tackle this problem, we propose to iteratively guess pseudo labels for the unlabeled image samples, which are later used to update the re-identification model together with the labelled samples.
Single-shot 3D shape reconstruction using deep convolutional neural networks
A robust single-shot 3D shape reconstruction technique integrating the fringe projection profilometry (FPP) technique with the deep convolutional neural networks (CNNs) is proposed in this letter.
Learning Structural Graph Layouts and 3D Shapes for Long Span Bridges 3D Reconstruction
Considering the prior human knowledge that these structures are in conformity to regular spatial layouts in terms of components, a learning-based topology-aware 3D reconstruction method which can obtain high-level structural graph layouts and low-level 3D shapes from images is proposed in this paper.
DISCO: Influence Maximization Meets Network Embedding and Deep Learning
As the number of required samples have been recently proven to be lower bounded by a particular threshold that presets tradeoff between the accuracy and efficiency, the result quality of these traditional solutions is hard to be further improved without sacrificing efficiency.
Towards End-to-End Text Spotting in Natural Scenes
Text spotting in natural scene images is of great importance for many image understanding tasks.
A Holistic Representation Guided Attention Network for Scene Text Recognition
In this work, we propose a simple yet strong approach for scene text recognition.
Pedestrian re-identification based on Tree branch network with local and global learning
Deep part-based methods in recent literature have revealed the great potential of learning local part-level representation for pedestrian image in the task of person re-identification.
RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems
Although various transfer learning methods have shown promising performance in this context, our proposed novel method RecSys-DAN focuses on alleviating the cross-domain and within-domain data sparsity and data imbalance and learns transferable latent representations for users, items and their interactions.
MMKG: Multi-Modal Knowledge Graphs
We present MMKG, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KGs.
Automatic trajectory measurement of large numbers of crowded objects
Complex motion patterns of natural systems, such as fish schools, bird flocks, and cell groups, have attracted great attention from scientists for years.
Compressive-Sensing Data Reconstruction for Structural Health Monitoring: A Machine-Learning Approach
The prior knowledge, i. e., the basis matrix and the CS-sampled signals, are used as the input and the target of the network; the basis coefficient matrix is embedded as the parameters of a certain layer; the objective function of conventional compressive sensing is set as the loss function of the network.
Double Neural Counterfactual Regret Minimization
Counterfactual Regret Minimization (CRF) is a fundamental and effective technique for solving Imperfect Information Games (IIG).
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
There are great interests as well as many challenges in applying reinforcement learning (RL) to recommendation systems.
Generative Adversarial Network
Model-based Reinforcement Learning
+3
Visual Question Answering as Reading Comprehension
In contrast to struggling on multimodal feature fusion, in this paper, we propose to unify all the input information by natural language so as to convert VQA into a machine reading comprehension problem.
MDLatLRR: A novel decomposition method for infrared and visible image fusion
We develop a novel image fusion framework based on MDLatLRR, which is used to decompose source images into detail parts(salient features) and base parts.
Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition
Recognizing irregular text in natural scene images is challenging due to the large variance in text appearance, such as curvature, orientation and distortion.
Ranked #26 on
Scene Text Recognition
on ICDAR2015
LRMM: Learning to Recommend with Missing Modalities
More importantly, LRMM is more robust to previous methods in alleviating data-sparsity and the cold-start problem.
Infrared and Visible Image Fusion with ResNet and zero-phase component analysis
Feature extraction and processing tasks play a key role in Image Fusion, and the fusion performance is directly affected by the different features and processing methods undertaken.
Adversarial Attack on Graph Structured Data
Deep learning on graph structures has shown exciting results in various applications.
General solutions for nonlinear differential equations: a rule-based self-learning approach using deep reinforcement learning
A universal rule-based self-learning approach using deep reinforcement learning (DRL) is proposed for the first time to solve nonlinear ordinary differential equations and partial differential equations.
Multi-focus Noisy Image Fusion using Low-Rank Representation
Multi-focus noisy image fusion represents an important task in the field of image fusion which generates a single, clear and focused image from all source images.
Infrared and visible image fusion using Latent Low-Rank Representation
Then, the low-rank parts are fused by weighted-average strategy to preserve more contour information.
DenseFuse: A Fusion Approach to Infrared and Visible Images
In this paper, we present a novel deep learning architecture for infrared and visible images fusion problem.
Multi-focus Image Fusion using dictionary learning and Low-Rank Representation
In this paper, we propose a novel multi-focus image fusion method based on dictionary learning and LRR to get a better performance in both global and local structure.
Infrared and Visible Image Fusion using a Deep Learning Framework
Then the base parts are fused by weighted-averaging.
Real-world Noisy Image Denoising: A New Benchmark
In order to promote the study on this problem while implementing the concurrent real-world image denoising datasets, we construct a new benchmark dataset which contains comprehensive real-world noisy images of different natural scenes.
Optimize transfer learning for lung diseases in bronchoscopy using a new concept: sequential fine-tuning
To help doctors to be more selective on biopsies and provide a second opinion on diagnosis, in this work, we propose a computer-aided diagnosis (CAD) system for lung diseases including cancers and tuberculosis (TB).
TransRev: Modeling Reviews as Translations from Users to Items
This is exploited in sentiment analysis where machine learning models are used to predict the review score from the text of the review.
Towards End-to-End Car License Plates Detection and Recognition with Deep Neural Networks
In contrast to existing approaches which take license plate detection and recognition as two separate tasks and settle them step by step, our method jointly solves these two tasks by a single network.
Push and Pull Search for Solving Constrained Multi-objective Optimization Problems
Compared with other CMOEAs, the proposed PPS method can more efficiently get across infeasible regions and converge to the feasible and non-dominated regions by applying push and pull search strategies at different stages.
On Multi-Relational Link Prediction with Bilinear Models
Bilinear models belong to the most basic models for this task, they are comparably efficient to train and use, and they can provide good prediction performance.
Towards End-to-end Text Spotting with Convolutional Recurrent Neural Networks
In this work, we jointly address the problem of text detection and recognition in natural scene images based on convolutional recurrent neural networks.
Bayesian System Identification based on Hierarchical Sparse Bayesian Learning and Gibbs Sampling with Application to Structural Damage Assessment
The focus in this paper is Bayesian system identification based on noisy incomplete modal data where we can impose spatially-sparse stiffness changes when updating a structural model.
Difficulty Adjustable and Scalable Constrained Multi-objective Test Problem Toolkit
Multi-objective evolutionary algorithms (MOEAs) have progressed significantly in recent decades, but most of them are designed to solve unconstrained multi-objective optimization problems.
Improved phase-unwrapping method using geometric constraints
In this paper, we proposed an improved method, which eliminates the system calibration and determination in Zhang's method, meanwhile does not need to use the low frequency fringe pattern.
Enhanced high dynamic range 3D shape measurement based on generalized phase-shifting algorithm
We proposed a method for enhanced high dynamic range 3D shape measurement based on generalized phase-shifting algorithm, which combines the complementary technique of inverted and regular fringe patterns with generalized phase-shifting algorithm.
Reading Car License Plates Using Deep Convolutional Neural Networks and LSTMs
Inspired by the success of deep neural networks (DNNs) in various vision applications, here we leverage DNNs to learn high-level features in a cascade framework, which lead to improved performance on both detection and recognition.
Salient Structure Detection by Context-Guided Visual Search
We define the task of salient structure (SS) detection to unify the saliency-related tasks like fixation prediction, salient object detection, and other detection of structures of interest.
Robust Bayesian compressive sensing with data loss recovery for structural health monitoring signals
The application of compressive sensing (CS) to structural health monitoring is an emerging research topic.
Worst-Case Linear Discriminant Analysis as Scalable Semidefinite Feasibility Problems
Our method is also much faster and more scalable than standard interior-point SDP solvers based WLDA.
KCRC-LCD: Discriminative Kernel Collaborative Representation with Locality Constrained Dictionary for Visual Categorization
The LCD similarity measure can be kernelized under KCRC, which theoretically links CRC and LCD under the kernel method.
Dictionary learning based image enhancement for rarity detection
Image enhancement is an important image processing technique that processes images suitably for a specific application e. g. image editing.
Solar Intranetwork Magnetic Elements: bipolar flux appearance
Each of the clusters is characterized by a few well-developed ERs that are partially or fully co-aligned in magnetic axis orientation.
Solar and Stellar Astrophysics
