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Found 21 papers, 4 papers with code
Triple Disentangled Representation Learning for Multimodal Affective Analysis
We revisit the disentanglement issue, and propose a novel triple disentanglement approach, TriDiRA, which disentangles the modality-invariant, effective modality-specific and ineffective modality-specific representations from input data.
No More Distractions: an Adaptive Up-Sampling Algorithm to Reduce Data Artifacts
Researchers recently found out that sometimes language models achieve high accuracy on benchmark data set, but they can not generalize very well with even little changes to the original data set.
Evaluating and Personalizing User-Perceived Quality of Text-to-Speech Voices for Delivering Mindfulness Meditation with Different Physical Embodiments
Building on findings from Phase 1, in Phase 2, an in-person within-subject study (N=94), we used a novel framework we developed for personalizing TTS voices based on user preferences, and evaluated user-perceived quality compared to best-rated non-personalized voices from Phase 1.
Evading Detection Actively: Toward Anti-Forensics against Forgery Localization
In adversarial training, SEAR employs a forgery localization model as a supervisor to explore tampering features and constructs a deep-learning concealer to erase corresponding traces.
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler
Nonetheless, because of the disparity of computing resources among different clients (i. e., device heterogeneity), synchronous federated learning algorithms suffer from degraded efficiency when waiting for straggler clients.
Synergistic Signal Denoising for Multimodal Time Series of Structure Vibration
Structural Health Monitoring (SHM) plays an indispensable role in ensuring the longevity and safety of infrastructure.
Transformer-Based Denoising of Mechanical Vibration Signals
Mechanical vibration signal denoising is a pivotal task in various industrial applications, including system health monitoring and failure prediction.
CSGCL: Community-Strength-Enhanced Graph Contrastive Learning
Graph Contrastive Learning (GCL) is an effective way to learn generalized graph representations in a self-supervised manner, and has grown rapidly in recent years.
Spatial-temporal Transformer-guided Diffusion based Data Augmentation for Efficient Skeleton-based Action Recognition
In this paper, we introduce a novel data augmentation method for skeleton-based action recognition tasks, which can effectively generate high-quality and diverse sequential actions.
Pose-Guided Graph Convolutional Networks for Skeleton-Based Action Recognition
Graph convolutional networks (GCNs), which can model the human body skeletons as spatial and temporal graphs, have shown remarkable potential in skeleton-based action recognition.
Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX
This study examines the efficacy of various neural network (NN) models in interpreting mental constructs via electroencephalogram (EEG) signals.
Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey
The availability of representative datasets is an essential prerequisite for many successful artificial intelligence and machine learning models.
Action Recognition with Domain Invariant Features of Skeleton Image
Due to the fast processing-speed and robustness it can achieve, skeleton-based action recognition has recently received the attention of the computer vision community.
A Teacher-Student Framework with Fourier Augmentation for COVID-19 Infection Segmentation in CT Images
Automatic segmentation of infected regions in computed tomography (CT) images is necessary for the initial diagnosis of COVID-19.
DFGC 2021: A DeepFake Game Competition
This competition provides a common platform for benchmarking the adversarial game between current state-of-the-art DeepFake creation and detection methods.
Identification and Avoidance of Static and Dynamic Obstacles on Point Cloud for UAVs Navigation
The approach is able to avoid both static obstacles and dynamic ones in the same framework.
Few-shot Learning for CT Scan based COVID-19 Diagnosis
To compensate for the sparseness of labeled data, the proposed method utilizes a large amount of synthetic COVID-19 CT images and adjusts the networks from the source domain (synthetic data) to the target domain (real data) with a cross-domain training mechanism.
Design and Implementation of Curriculum System Based on Knowledge Graph
With the fact that the knowledge in each field in university is keeping increasing, the number of university courses is becoming larger, and the content and curriculum system is becoming much more complicated than it used to be, which bring many inconveniences to the course arrangement and analysis.
Unsupervised domain adaptation based COVID-19 CT infection segmentation network
In this paper, we propose an unsupervised domain adaptation based segmentation network to improve the segmentation performance of the infection areas in COVID-19 CT images.
COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Network
However, training a deep-learning model requires large volumes of data, and medical staff faces a high risk when collecting COVID-19 CT data due to the high infectivity of the disease.
Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression
The two main differences between the convex and non-convex approach are: (i) from a computational perspective whether the non-convex projection operator is computable and whether the projection has desirable contraction properties and (ii) from a statistical upper bound perspective, the non-convex approach has a superior rate for a number of examples.
