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Found 88 papers, 26 papers with code
PraNet: Parallel Reverse Attention Network for Polyp Segmentation
To address these challenges, we propose a parallel reverse attention network (PraNet) for accurate polyp segmentation in colonoscopy images.
Ranked #7 on
Video Polyp Segmentation
on SUN-SEG-Easy (Unseen)
Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images
Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis.
Specificity-preserving RGB-D Saliency Detection
To effectively fuse cross-modal features in the shared learning network, we propose a cross-enhanced integration module (CIM) and then propagate the fused feature to the next layer for integrating cross-level information.
Can SAM Segment Polyps?
Recently, Meta AI Research releases a general Segment Anything Model (SAM), which has demonstrated promising performance in several segmentation tasks.
Hi-Net: Hybrid-fusion Network for Multi-modal MR Image Synthesis
Medical image synthesis has been proposed as an effective solution to this, where any missing modalities are synthesized from the existing ones.
Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model
Thus, how to utilize such a large foundation model for medical image segmentation is an emerging research target.
Context-aware Cross-level Fusion Network for Camouflaged Object Detection
Camouflaged object detection (COD) is a challenging task due to the low boundary contrast between the object and its surroundings.
threaTrace: Detecting and Tracing Host-based Threats in Node Level Through Provenance Graph Learning
Recent studies propose leveraging the rich contextual information in data provenance to detect threats in a host.
Light Field Salient Object Detection: A Review and Benchmark
Secondly, we benchmark nine representative light field SOD models together with several cutting-edge RGB-D SOD models on four widely used light field datasets, from which insightful discussions and analyses, including a comparison between light field SOD and RGB-D SOD models, are achieved.
Camouflaged Object Detection via Context-aware Cross-level Fusion
To address these challenges, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects.
On Vision Features in Multimodal Machine Translation
Previous work on multimodal machine translation (MMT) has focused on the way of incorporating vision features into translation but little attention is on the quality of vision models.
Real-time sparse-sampled Ptychographic imaging through deep neural networks
Ptychography has rapidly grown in the fields of X-ray and electron imaging for its unprecedented ability to achieve nano or atomic scale resolution while simultaneously retrieving chemical or magnetic information from a sample.
PPR-Net:Point-wise Pose Regression Network for Instance Segmentation and 6D Pose Estimation in Bin-picking Scenarios
For each point in the point cloud, the network regresses a 6D pose of the object instance that the point belongs to.
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation
Inspired by this, we design a new architecture, {\it ODE Transformer}, which is analogous to the Runge-Kutta method that is well motivated in ODE.
Multi-scale Transformer Network with Edge-aware Pre-training for Cross-Modality MR Image Synthesis
To take advantage of both paired and unpaired data, in this paper, we propose a Multi-scale Transformer Network (MT-Net) with edge-aware pre-training for cross-modality MR image synthesis.
SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis.
Feature Aggregation and Propagation Network for Camouflaged Object Detection
In this paper, we propose a novel Feature Aggregation and Propagation Network (FAP-Net) for camouflaged object detection.
SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation
Our new method is iterative and consists of two main stages: (1) segmentation model training; (2) expanding the labeled set by using the trained segmentation model, an unlabeled set, SAM, and domain-specific knowledge.
Predicting Missing Links via Local Information
Missing link prediction of networks is of both theoretical interest and practical significance in modern science.
Ranked #1 on
Link Property Prediction
on ogbl-ppa
Data Analysis, Statistics and Probability Physics and Society
ParaDiag: parallel-in-time algorithms based on the diagonalization technique
These results are obtained on the Tianhe-1 supercomputer in China and the SIUE Campus Cluster in the US and and we compare these results to the performance of parareal and MGRiT, two widely used PinT algorithms.
Numerical Analysis Numerical Analysis
A generalized method toward drug-target interaction prediction via low-rank matrix projection
On one hand, when there is no extra characteristic information of drugs or targets, the proposed method utilizes only the known interactions.
Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation
Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor using the intermediate data distributions generated for the two domains, which includes an image-to-image translator and a shared-weighted segmentation network.
SAM-PD: How Far Can SAM Take Us in Tracking and Segmenting Anything in Videos by Prompt Denoising
Recently, promptable segmentation models, such as the Segment Anything Model (SAM), have demonstrated robust zero-shot generalization capabilities on static images.
Recommender Systems
In this article, we review recent developments in recommender systems and discuss the major challenges.
Dynamic Weighted Adversarial Learning for Semi-Supervised Classification under Intersectional Class Mismatch
Nowadays, class-mismatch problem has drawn intensive attention in Semi-Supervised Learning (SSL), where the classes of labeled data are assumed to be only a subset of the classes of unlabeled data.
Attention-based Natural Language Person Retrieval
Following the recent progress in image classification and captioning using deep learning, we develop a novel natural language person retrieval system based on an attention mechanism.
Link Prediction via Matrix Completion
Inspired by practical importance of social networks, economic networks, biological networks and so on, studies on large and complex networks have attracted a surge of attentions in the recent years.
Patch-based Texture Synthesis for Image Inpainting
We form it as an optimization problem that identifies the potential patches for synthesis from an coarse-to-fine manner.
Robust Visual Tracking via Inverse Nonnegative Matrix Factorization
It utilizes both the foreground and background information, and imposes a local coordinate constraint, where the basis matrix is sparse matrix from the linear combination of candidates with corresponding nonnegative coefficient vectors.
Visual Tracking via Nonnegative Regularization Multiple Locality Coding
Weights of these various dictionaries are also learned from approximated LLC in the similar framework.
Customer Sharing in Economic Networks with Costs
To tackle this problem, we develop a novel mechanism called customer sharing mechanism (CSM) which incentivizes all sellers to share each other's sale information to their private customer groups.
Payoff Control in the Iterated Prisoner's Dilemma
Repeated game has long been the touchstone model for agents' long-run relationships.
Learning to Sketch with Deep Q Networks and Demonstrated Strokes
Doodling is a useful and common intelligent skill that people can learn and master.
Robust Visual Tracking Revisited: From Correlation Filter to Template Matching
In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs).
Computational Socioeconomics
Uncovering the structure of socioeconomic systems and timely estimation of socioeconomic status are significant for economic development.
Hierarchical Clustering Supported by Reciprocal Nearest Neighbors
Clustering is a fundamental analysis tool aiming at classifying data points into groups based on their similarity or distance.
Exploration of Surgeons' Natural Skills for Robotic Catheterization
Despite having the robotic catheter systems which have recently emerged as safe way of performing cardiovascular interventions, a number of important challenges are yet to be investigated.
Contrast-weighted Dictionary Learning Based Saliency Detection for Remote Sensing Images
Furthermore, by using the proposed joint saliency measure, a variety of saliency maps are generated based on the discriminant dictionary.
Multi-Mutual Consistency Induced Transfer Subspace Learning for Human Motion Segmentation
Human motion segmentation based on transfer subspace learning is a rising interest in action-related tasks.
M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients
Then, we use a modality-specific network to extract implicit and high-level features from different MR scans.
Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images
Diagnosis from chest CT images is a promising direction.
Multi-level Cross-modal Interaction Network for RGB-D Salient Object Detection
Our MCI-Net includes two key components: 1) a cross-modal feature learning network, which is used to learn the high-level features for the RGB images and depth cues, effectively enabling the correlations between the two sources to be exploited; and 2) a multi-level interactive integration network, which integrates multi-level cross-modal features to boost the SOD performance.
Dynamic Fusion based Federated Learning for COVID-19 Detection
To improve communication efficiency and model performance, in this paper, we propose a novel dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections.
Mechanism Design in Social Networks
The challenge to the seller is to design a mechanism to incentivize the buyers, who are aware of the auction, to further propagate the information to their neighbors so that more buyers will participate in the auction and hence, the seller will be able to make a higher revenue.
Computer Science and Game Theory
High resolution functional imaging through Lorentz transmission electron microscopy and differentiable programming
Lorentz transmission electron microscopy is a unique characterization technique that enables the simultaneous imaging of both the microstructure and functional properties of materials at high spatial resolution.
Improving EEG Decoding via Clustering-based Multi-task Feature Learning
With the encoded label matrix, we devise a novel multi-task learning algorithm by exploiting the subclass relationship to jointly optimize the EEG pattern features from the uncovered subclasses.
Progresses and Challenges in Link Prediction
Link prediction is a paradigmatic problem in network science, which aims at estimating the existence likelihoods of nonobserved links, based on known topology.
Collaborative Filtering Approach to Link Prediction
Considering the delicate but important difference between personalized recommendation and link prediction, we further propose an improved framework named as self-included collaborative filtering (SCF).
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Neural Machine Translation
We show that a residual block of layers in Transformer can be described as a higher-order solution to ODEs.
Normalizing field flows: Solving forward and inverse stochastic differential equations using physics-informed flow models
We introduce in this work the normalizing field flows (NFF) for learning random fields from scattered measurements.
Information Cocoons in Online Navigation
Social media and online navigation bring us enjoyable experience in accessing information, and simultaneously create information cocoons (ICs) in which we are unconsciously trapped with limited and biased information.
The NiuTrans Machine Translation Systems for WMT21
This paper describes NiuTrans neural machine translation systems of the WMT 2021 news translation tasks.
Visual-Textual Attentive Semantic Consistency for Medical Report Generation
Second, the progressive report decoder consists of a sentence decoder and a word decoder, where we propose image-sentence matching and description accuracy losses to constrain the visual-textual semantic consistency.
CCT-Net: Category-Invariant Cross-Domain Transfer for Medical Single-to-Multiple Disease Diagnosis
A category-invariant cross-domain transfer (CCT) method is proposed to address this single-to-multiple extension.
The NiuTrans Machine Translation Systems for WMT20
This paper describes NiuTrans neural machine translation systems of the WMT20 news translation tasks.
Solving time dependent Fokker-Planck equations via temporal normalizing flow
In this work, we propose an adaptive learning approach based on temporal normalizing flows for solving time-dependent Fokker-Planck (TFP) equations.
Consistency and Diversity induced Human Motion Segmentation
Besides, a novel constraint based on the Hilbert Schmidt Independence Criterion (HSIC) is introduced to ensure the diversity of multi-level subspace representations, which enables the complementarity of multi-level representations to be explored to boost the transfer learning performance.
Monte Carlo PINNs: deep learning approach for forward and inverse problems involving high dimensional fractional partial differential equations
We introduce a sampling based machine learning approach, Monte Carlo physics informed neural networks (MC-PINNs), for solving forward and inverse fractional partial differential equations (FPDEs).
Multi-class Label Noise Learning via Loss Decomposition and Centroid Estimation
Further, by defining a new form of data centroid, we transform the recovery problem of a label-dependent part to a centroid estimation problem.
Deep learning at the edge enables real-time streaming ptychographic imaging
Coherent microscopy techniques provide an unparalleled multi-scale view of materials across scientific and technological fields, from structural materials to quantum devices, from integrated circuits to biological cells.
Myopia prediction for adolescents via time-aware deep learning
Result: The mean absolute prediction error on the testing set was 0. 273-0. 257 for spherical equivalent, ranging from 0. 189-0. 160 to 0. 596-0. 473 if we consider different lengths of historical records and different prediction durations.
Prompt-driven efficient Open-set Semi-supervised Learning
In this paper, we propose a prompt-driven efficient OSSL framework, called OpenPrompt, which can propagate class information from labeled to unlabeled data with only a small number of trainable parameters.
Failure-informed adaptive sampling for PINNs
For instance, a fixed set of (prior chosen) training points may fail to capture the effective solution region (especially for problems with singularities).
Adaptive deep density approximation for fractional Fokker-Planck equations
To this end, we represent the solution with an explicit PDF model induced by a flow-based deep generative model, simplified KRnet, which constructs a transport map from a simple distribution to the target distribution.
SOLAR: A Highly Optimized Data Loading Framework for Distributed Training of CNN-based Scientific Surrogates
Our evaluation with three scientific surrogates and 32 GPUs illustrates that SOLAR can achieve up to 24. 4X speedup over PyTorch Data Loader and 3. 52X speedup over state-of-the-art data loaders.
A Structure-guided Effective and Temporal-lag Connectivity Network for Revealing Brain Disorder Mechanisms
Brain network provides important insights for the diagnosis of many brain disorders, and how to effectively model the brain structure has become one of the core issues in the domain of brain imaging analysis.
Prompting Neural Machine Translation with Translation Memories
Improving machine translation (MT) systems with translation memories (TMs) is of great interest to practitioners in the MT community.
Failure-informed adaptive sampling for PINNs, Part II: combining with re-sampling and subset simulation
The second extension is to present the subset simulation algorithm as the posterior model (instead of the truncated Gaussian model) for estimating the error indicator, which can more effectively estimate the failure probability and generate new effective training points in the failure region.
IB-UQ: Information bottleneck based uncertainty quantification for neural function regression and neural operator learning
We propose a novel framework for uncertainty quantification via information bottleneck (IB-UQ) for scientific machine learning tasks, including deep neural network (DNN) regression and neural operator learning (DeepONet).
Self-Paced Learning for Open-Set Domain Adaptation
Open-set domain adaptation aims to not only recognize target samples belonging to common classes shared by source and target domains but also perceive unknown class samples.
AI-assisted Automated Workflow for Real-time X-ray Ptychography Data Analysis via Federated Resources
We present an end-to-end automated workflow that uses large-scale remote compute resources and an embedded GPU platform at the edge to enable AI/ML-accelerated real-time analysis of data collected for x-ray ptychography.
Bounded KRnet and its applications to density estimation and approximation
In this paper, we develop an invertible mapping, called B-KRnet, on a bounded domain and apply it to density estimation/approximation for data or the solutions of PDEs such as the Fokker-Planck equation and the Keller-Segel equation.
Collaborative Recommendation Model Based on Multi-modal Multi-view Attention Network: Movie and literature cases
Thus, the representation of users' dislikes should be integrated into the user modelling when we construct a collaborative recommendation model.
COCA: Classifier-Oriented Calibration via Textual Prototype for Source-Free Universal Domain Adaptation
SF-UniDA methods eliminate the need for direct access to source samples when performing adaptation to the target domain.
Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference
Asynchronous time series, also known as temporal event sequences, are the basis of many applications throughout different industries.
Edge-aware Feature Aggregation Network for Polyp Segmentation
Besides, a Scale-aware Convolution Module (SCM) is proposed to learn scale-aware features by using dilated convolutions with different ratios, in order to effectively deal with scale variation.
F&F Attack: Adversarial Attack against Multiple Object Trackers by Inducing False Negatives and False Positives
Multi-object tracking (MOT) aims to build moving trajectories for number-agnostic objects.
Memory-Assisted Sub-Prototype Mining for Universal Domain Adaptation
To address these issues, we propose a novel Memory-Assisted Sub-Prototype Mining (MemSPM) method that can learn the differences between samples belonging to the same category and mine sub-classes when there exists significant concept shift between them.
Adaptive importance sampling for Deep Ritz
The first step is solving the PDEs using the Deep Ritz method by minimizing an associated variational loss discretized by the collocation points in the training set.
Adaptive operator learning for infinite-dimensional Bayesian inverse problems
To this end, we develop an adaptive operator learning framework that can reduce modeling error gradually by forcing the surrogate to be accurate in local areas.
A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends
This paper provides a comprehensive review of polyp segmentation algorithms.
Segment Anything Model-guided Collaborative Learning Network for Scribble-supervised Polyp Segmentation
Specifically, we first propose a Cross-level Enhancement and Aggregation Network (CEA-Net) for weakly-supervised polyp segmentation.
Generalized Damping Torque Analysis of Ultra-Low Frequency Oscillation in the Jerk Space
A multi-information variable is constructed to transform the system into a new state space, where it is found that the jerk dynamics of the turbine-generator cascaded system is a second-order differential equation.
SQA-SAM: Segmentation Quality Assessment for Medical Images Utilizing the Segment Anything Model
Segmentation quality assessment (SQA) plays a critical role in the deployment of a medical image based AI system.
Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Polyp Segmentation
To address scale variation, we present a scale-enhanced consistency constraint, which ensures consistency in the segmentation maps generated from the same input image at different scales.
Energy based diffusion generator for efficient sampling of Boltzmann distributions
We introduce a novel sampler called the energy based diffusion generator for generating samples from arbitrary target distributions.
PokerGPT: An End-to-End Lightweight Solver for Multi-Player Texas Hold'em via Large Language Model
To improve fine-tuning performance, we conduct prompt engineering on raw data, including filtering useful information, selecting behaviors of players with high win rates, and further processing them into textual instruction using multiple prompt engineering techniques.
