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Found 43 papers, 13 papers with code
A Novel Perspective for Multi-modal Multi-label Skin Lesion Classification
The efficacy of deep learning-based Computer-Aided Diagnosis (CAD) methods for skin diseases relies on analyzing multiple data modalities (i. e., clinical+dermoscopic images, and patient metadata) and addressing the challenges of multi-label classification.
A Comprehensive Survey on Deep Multimodal Learning with Missing Modality
During multimodal model training and reasoning, data samples may miss certain modalities and lead to compromised model performance due to sensor limitations, cost constraints, privacy concerns, data loss, and temporal and spatial factors.
Human-AI Collaborative Multi-modal Multi-rater Learning for Endometriosis Diagnosis
However, even experienced clinicians struggle with accurately classifying POD obliteration from MRI images, which complicates the training of reliable AI models.
Deformable Feature Alignment and Refinement for Moving Infrared Dim-small Target Detection
Specifically, a Temporal Deformable Alignment (TDA) module based on the designed Dilated Convolution Attention Fusion (DCAF) block is developed to explicitly align the adjacent frames with the current frame at the feature level.
ItTakesTwo: Leveraging Peer Representations for Semi-supervised LiDAR Semantic Segmentation
Additionally, these SSL approaches employ contrastive learning based on the sampling from a limited set of positive and negative embedding samples.
CPM: Class-conditional Prompting Machine for Audio-visual Segmentation
However, the inherent training issues of transformer-based methods, such as the low efficacy of cross-attention and unstable bipartite matching, can be amplified in AVS, particularly when the learned audio query does not provide a clear semantic clue.
Enhancing Multi-modal Learning: Meta-learned Cross-modal Knowledge Distillation for Handling Missing Modalities
This cross-modal knowledge distillation produces a highly accurate model even with the absence of influential modalities.
Disentangling Specificity for Abstractive Multi-document Summarization
Multi-document summarization (MDS) generates a summary from a document set.
Segment Beyond View: Handling Partially Missing Modality for Audio-Visual Semantic Segmentation
The model consists of a vision teacher utilising panoramic information, an auditory teacher with 8-channel audio, and an audio-visual student that takes views with limited FoV and binaural audio as input and produce semantic segmentation for objects outside FoV.
Dynamically configured physics-informed neural network in topology optimization applications
The designed architecture aims to dynamically configure trainable parameters; that is, an inexpensive NN is used to replace an expensive one at certain optimization cycles.
Learnable Cross-modal Knowledge Distillation for Multi-modal Learning with Missing Modality
Then, cross-modal knowledge distillation is performed between teacher and student modalities for each task to push the model parameters to a point that is beneficial for all tasks.
Multi-modal Learning with Missing Modality via Shared-Specific Feature Modelling
Current methods aiming to handle the missing modality problem in multi-modal tasks, either deal with missing modalities only during evaluation or train separate models to handle specific missing modality settings.
Distilling Missing Modality Knowledge from Ultrasound for Endometriosis Diagnosis with Magnetic Resonance Images
Next, we distill the knowledge from the teacher TVUS POD obliteration detector to train the student MRI model by minimizing a regression loss that approximates the output of the student to the teacher using unpaired TVUS and MRI data.
Unraveling Instance Associations: A Closer Look for Audio-Visual Segmentation
We show empirical results that demonstrate the effectiveness of our benchmark.
Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation
When analysing screening mammograms, radiologists can naturally process information across two ipsilateral views of each breast, namely the cranio-caudal (CC) and mediolateral-oblique (MLO) views.
M^4I: Multi-modal Models Membership Inference
To achieve this, we propose Multi-modal Models Membership Inference (M^4I) with two attack methods to infer the membership status, named metric-based (MB) M^4I and feature-based (FB) M^4I, respectively.
Document-aware Positional Encoding and Linguistic-guided Encoding for Abstractive Multi-document Summarization
One effective way is to encode document positional information to assist models in capturing cross-document relations.
KUNet: Imaging Knowledge-Inspired Single HDR Image Reconstruction
Recently, with the rise of high dynamic range (HDR) display devices, there is a great demand to transfer traditional low dynamic range (LDR) images into HDR versions.
Uncertainty-aware Multi-modal Learning via Cross-modal Random Network Prediction
Multi-modal learning focuses on training models by equally combining multiple input data modalities during the prediction process.
BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification
Deep learning methods have shown outstanding classification accuracy in medical imaging problems, which is largely attributed to the availability of large-scale datasets manually annotated with clean labels.
Risk and return prediction for pricing portfolios of non-performing consumer credit
We design a system for risk-analyzing and pricing portfolios of non-performing consumer credit loans.
Dependency Structure for News Document Summarization
In this work, we develop a neural network based model which leverages dependency parsing to capture cross-positional dependencies and grammatical structures.
Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography
The advancement of secure communication and identity verification fields has significantly increased through the use of deep learning techniques for data hiding.
Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning
With this key observation, we protect data privacy and allow the disclosure of feature meaning by concealing decision paths and adapt a communication-efficient secure computation method for inference outputs.
Kernel Adversarial Learning for Real-world Image Super-resolution
Current deep image super-resolution (SR) approaches aim to restore high-resolution images from down-sampled images or by assuming degradation from simple Gaussian kernels and additive noises.
Oriole: Thwarting Privacy against Trustworthy Deep Learning Models
Deep Neural Networks have achieved unprecedented success in the field of face recognition such that any individual can crawl the data of others from the Internet without their explicit permission for the purpose of training high-precision face recognition models, creating a serious violation of privacy.
Delayed Rewards Calibration via Reward Empirical Sufficiency
Therefore, a purify-trained classifier is designed to obtain the distribution and generate the calibrated rewards.
Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline
To address these issues, in this work we carefully design our settings and propose a new dataset including both synthetic and real traffic data in more complex scenarios.
Memory-Gated Recurrent Networks
The essence of multivariate sequential learning is all about how to extract dependencies in data.
Fully Quantized Image Super-Resolution Networks
With the rising popularity of intelligent mobile devices, it is of great practical significance to develop accurate, realtime and energy-efficient image Super-Resolution (SR) inference methods.
Robust Data Hiding Using Inverse Gradient Attention
In deep data hiding models, to maximize the encoding capacity, each pixel of the cover image ought to be treated differently since they have different sensitivities w. r. t.
Multi-document Summarization via Deep Learning Techniques: A Survey
Multi-document summarization (MDS) is an effective tool for information aggregation that generates an informative and concise summary from a cluster of topic-related documents.
Randomized Online CP Decomposition
The experimental results on synthetic data and real-world data show the ROCP algorithm is able to cope with CP decomposition for large-scale tensors with arbitrary number of dimensions.
Soft Expert Reward Learning for Vision-and-Language Navigation
In this paper, we introduce a Soft Expert Reward Learning (SERL) model to overcome the reward engineering designing and generalisation problems of the VLN task.
Multi-label Thoracic Disease Image Classification with Cross-Attention Networks
Automated disease classification of radiology images has been emerging as a promising technique to support clinical diagnosis and treatment planning.
Unsupervised Representation Learning by Predicting Random Distances
To enable unsupervised learning on those domains, in this work we propose to learn features without using any labelled data by training neural networks to predict data distances in a randomly projected space.
Image-based reconstruction for the impact problems by using DPNNs
Therefore, in this study, an improved ReConNN method is proposed to address the mentioned weaknesses.
Image-Based Reconstruction for a 3D-PFHS Heat Transfer Problem by ReConNN
Therefore, an image-based reconstruction model of a heat transfer process for a 3D-PFHS is established.
An Adaptive Population Size Differential Evolution with Novel Mutation Strategy for Constrained Optimization
To achieve the aim of searching the feasible solutions accurately, an adaptive population size method and an adaptive mutation strategy are proposed in the paper.
Reconstruction of Simulation-Based Physical Field by Reconstruction Neural Network Method
In such a framework, a reconstruction neural network (ReConNN) model designed for simulation-based physical field's reconstruction is proposed.
ReNN: Rule-embedded Neural Networks
The complexity of neural networks can be reduced since long-term dependencies are not modeled with neural connections, and thus the amount of data needed to optimize the neural networks can be reduced.
Can CNN Construct Highly Accurate Models Efficiently for High-Dimensional Problems in Complex Product Designs?
In order to evaluate the proposed CNN metamodel for hundreds-dimensional and strong nonlinear problems, CNN is compared with other metamodeling techniques.
How we can control the crack to propagate along the specified path feasibly?
A controllable crack propagation (CCP) strategy is suggested.
