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Found 46 papers, 13 papers with code
Electroencephalogram-based Multi-class Decoding of Attended Speakers' Direction with Audio Spatial Spectrum
In this paper, we observe that, on our recently presented dataset with 15-class directional focus, models relying exclusively on EEG inputs exhibits significantly lower accuracy when decoding the directional focus in both leave-one-subject-out and leave-one-trial-out scenarios.
Pathologist-like explainable AI for interpretable Gleason grading in prostate cancer
The aggressiveness of prostate cancer, the most common cancer in men worldwide, is primarily assessed based on histopathological data using the Gleason scoring system.
Unrolling Plug-and-Play Gradient Graph Laplacian Regularizer for Image Restoration
Generic deep learning (DL) networks for image restoration like denoising and interpolation lack mathematical interpretability, require voluminous training data to tune a large parameter set, and are fragile in the face of covariate shift.
MULAN: A Multi Layer Annotated Dataset for Controllable Text-to-Image Generation
To build MuLAn, we developed a training free pipeline which decomposes a monocular RGB image into a stack of RGBA layers comprising of background and isolated instances.
Accelerating Diffusion Sampling with Optimized Time Steps
While this is a significant development, most sampling methods still employ uniform time steps, which is not optimal when using a small number of steps.
TAnet: A New Temporal Attention Network for EEG-based Auditory Spatial Attention Decoding with a Short Decision Window
Auditory spatial attention detection (ASAD) is used to determine the direction of a listener's attention to a speaker by analyzing her/his electroencephalographic (EEG) signals.
On Topological Conditions for Enabling Transient Control in Leader-follower Networks
We derive necessary and sufficient conditions for leader-follower multi-agent systems such that we can further apply prescribed performance control to achieve the desired formation while satisfying certain transient constraints.
Non-Intrusive Speech Intelligibility Prediction for Hearing Aids using Whisper and Metadata
Automated speech intelligibility assessment is pivotal for hearing aid (HA) development.
BiRP: Learning Robot Generalized Bimanual Coordination using Relative Parameterization Method on Human Demonstration
We demonstrate the method using synthetic motions and human demonstration data and deploy it to a humanoid robot to perform a generalized bimanual coordination motion.
SoftGPT: Learn Goal-oriented Soft Object Manipulation Skills by Generative Pre-trained Heterogeneous Graph Transformer
Soft object manipulation tasks in domestic scenes pose a significant challenge for existing robotic skill learning techniques due to their complex dynamics and variable shape characteristics.
Deep learning radiomics for assessment of gastroesophageal varices in people with compensated advanced chronic liver disease
Validation analysis revealed that the AUCs of DLRP were 0. 91 for GEV (95% CI 0. 90 to 0. 93, p < 0. 05) and 0. 88 for HRV (95% CI 0. 86 to 0. 89, p < 0. 01), which were significantly and robustly better than canonical risk indicators, including the value of LSM and SSM.
Calibrationless Reconstruction of Uniformly-Undersampled Multi-Channel MR Data with Deep Learning Estimated ESPIRiT Maps
Further, they led to excellent ESPIRiT reconstruction performance even at high acceleration, exhibiting a similar level of errors and artifacts to that by using reference ESPIRiT maps.
Dual-Curriculum Teacher for Domain-Inconsistent Object Detection in Autonomous Driving
Specifically, DucTeacher consists of two curriculums, i. e., (1) domain evolving curriculum seeks to learn from the data progressively to handle data distribution discrepancy by estimating the similarity between domains, and (2) distribution matching curriculum seeks to estimate the class distribution for each unlabeled domain to handle class distribution shifts.
6DOF Pose Estimation of a 3D Rigid Object based on Edge-enhanced Point Pair Features
We introduce a well-targeted down-sampling strategy that focuses more on edge area for efficient feature extraction of complex geometry.
A Consistency Constraint-Based Approach to Coupled State Constraints in Distributed Model Predictive Control
In this paper, we present a distributed model predictive control (DMPC) scheme for dynamically decoupled systems which are subject to state constraints, coupling state constraints and input constraints.
Less is More: Adaptive Curriculum Learning for Thyroid Nodule Diagnosis
Thyroid nodule classification aims at determining whether the nodule is benign or malignant based on a given ultrasound image.
Manifold Graph Signal Restoration using Gradient Graph Laplacian Regularizer
In this paper, focusing on manifold graphs -- collections of uniform discrete samples on low-dimensional continuous manifolds -- we generalize GLR to gradient graph Laplacian regularizer (GGLR) that promotes planar / piecewise planar (PWP) signal reconstruction.
XBound-Former: Toward Cross-scale Boundary Modeling in Transformers
Skin lesion segmentation from dermoscopy images is of great significance in the quantitative analysis of skin cancers, which is yet challenging even for dermatologists due to the inherent issues, i. e., considerable size, shape and color variation, and ambiguous boundaries.
GLObal Building heights for Urban Studies (UT-GLOBUS) for city- and street- scale urban simulations: Development and first applications
We introduce University of Texas - Global Building heights for Urban Studies (UT-GLOBUS), a dataset providing building heights and urban canopy parameters (UCPs) for more than 1200 cities or locales worldwide.
Robot Cooking with Stir-fry: Bimanual Non-prehensile Manipulation of Semi-fluid Objects
This letter describes an approach to achieve well-known Chinese cooking art stir-fry on a bimanual robot system.
MTI-Net: A Multi-Target Speech Intelligibility Prediction Model
Recently, deep learning (DL)-based non-intrusive speech assessment models have attracted great attention.
MBI-Net: A Non-Intrusive Multi-Branched Speech Intelligibility Prediction Model for Hearing Aids
In this study, we propose a multi-branched speech intelligibility prediction model (MBI-Net), for predicting the subjective intelligibility scores of HA users.
Fast Computation of Generalized Eigenvectors for Manifold Graph Embedding
Experiments show that our embedding is among the fastest in the literature, while producing the best clustering performance for manifold graphs.
Parallelized Robust Distributed Model Predictive Control in the Presence of Coupled State Constraints
Reference trajectories and consistency constraints are known to neighboring subsystems.
Deep Learning-based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Features
In this study, we propose a cross-domain multi-objective speech assessment model called MOSA-Net, which can estimate multiple speech assessment metrics simultaneously.
Learning Robotic Ultrasound Scanning Skills via Human Demonstrations and Guided Explorations
Medical ultrasound has become a routine examination approach nowadays and is widely adopted for different medical applications, so it is desired to have a robotic ultrasound system to perform the ultrasound scanning autonomously.
LV-BERT: Exploiting Layer Variety for BERT
In this paper, beyond this stereotyped layer pattern, we aim to improve pre-trained models by exploiting layer variety from two aspects: the layer type set and the layer order.
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data
Motivated by the above findings, we propose a novel and simple algorithm called Classifier Calibration with Virtual Representations (CCVR), which adjusts the classifier using virtual representations sampled from an approximated gaussian mixture model.
Grapevine Winter Pruning Automation: On Potential Pruning Points Detection through 2D Plant Modeling using Grapevine Segmentation
Grapevine winter pruning is a complex task, that requires skilled workers to execute it correctly.
Whole-Body Control on Non-holonomic Mobile Manipulation for Grapevine Winter Pruning Automation
In this paper, we explore a whole-body motion controller of a robot which is composed of a 2-DoFs non-holonomic wheeled mobile base with a 7-DoFs manipulator (non-holonomic wheeled mobile manipulator, NWMM) This robotic platform is designed to efficiently undertake complex grapevine pruning tasks.
Two stages for visual object tracking
Then more accurate tracking results are obtained by segmentation module given the coarse state estimation in the first stage.
Intuitive Tasks Planning Using Visuo-Tactile Perception for Human Robot Cooperation
Designing robotic tasks for co-manipulation necessitates to exploit not only proprioceptive but also exteroceptive information for improved safety and autonomy.
Eye-gaze Estimation with HEOG and Neck EMG using Deep Neural Networks
These results indicated the feasibility to estimate eye-gaze with HEOG and NEMG.
The effect of speech and noise levels on the quality perceived by cochlear implant and normal hearing listeners
Noise reduction (NR) algorithms used in CI reduce the noise in favor of signal-to-noise ratio (SNR), regardless of plausible accompanying distortions that may degrade the speech quality perception.
Fast & Robust Image Interpolation using Gradient Graph Laplacian Regularizer
In the graph signal processing (GSP) literature, it has been shown that signal-dependent graph Laplacian regularizer (GLR) can efficiently promote piecewise constant (PWC) signal reconstruction for various image restoration tasks.
MetaAugment: Sample-Aware Data Augmentation Policy Learning
Automated data augmentation has shown superior performance in image recognition.
Reproducible Pruning System on Dynamic Natural Plants for Field Agricultural Robots
The Natural Admittance Controller (NAC) is applied to deal with the dynamics of vines.
Robotics Systems and Control Systems and Control
The Importance and the Limitations of Sim2Real for Robotic Manipulation in Precision Agriculture
In recent years Sim2Real approaches have brought great results to robotics.
Robotics
Risk Variance Penalization
The key of the out-of-distribution (OOD) generalization is to generalize invariance from training domains to target domains.
MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection
Recommender systems often face heterogeneous datasets containing highly personalized historical data of users, where no single model could give the best recommendation for every user.
Dynamic Spatio-temporal Graph-based CNNs for Traffic Prediction
To capture the graph dynamics, we use the graph prediction stream to predict the dynamic graph structures, and the predicted structures are fed into the flow prediction stream.
Federated Meta-Learning with Fast Convergence and Efficient Communication
Statistical and systematic challenges in collaboratively training machine learning models across distributed networks of mobile devices have been the bottlenecks in the real-world application of federated learning.
Real-Time Localization and Tracking of Multiple Radio-Tagged Animals with an Autonomous UAV
Autonomous aerial robots provide new possibilities to study the habitats and behaviors of endangered species through the efficient gathering of location information at temporal and spatial granularities not possible with traditional manual survey methods.
Systems and Control Robotics
Meta-SGD: Learning to Learn Quickly for Few-Shot Learning
In contrast, meta-learning learns from many related tasks a meta-learner that can learn a new task more accurately and faster with fewer examples, where the choice of meta-learners is crucial.
External Patch Prior Guided Internal Clustering for Image Denoising
One category of denoising methods exploit the priors (e. g., TV, sparsity) learned from external clean images to reconstruct the given noisy image, while another category of methods exploit the internal prior (e. g., self-similarity) to reconstruct the latent image.
Deep Learning Shape Priors for Object Segmentation
In this paper we introduce a new shape-driven approach for object segmentation.
