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Found 20 papers, 10 papers with code
Point Cloud Self-supervised Learning via 3D to Multi-view Masked Autoencoder
However, a notable limitation of these approaches is that they do not fully utilize the multi-view attributes inherent in 3D point clouds, which is crucial for a deeper understanding of 3D structures.
EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations
In addition to our thorough evaluation and analysis on eight existing datasets based on the benchmarking literature, we release two new benchmark datasets, propose four new metrics, and three challenging tasks.
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DNN-DANM: A High-Accuracy Two-Dimensional DOA Estimation Method Using Practical RIS
After formulating the system model with the mutual coupling effect and the reflection phase/amplitude errors of the RIS, a novel DNNDANM method is proposed for the DOA estimation by combining the deep neural network (DNN) and the decoupling atomic norm minimization (DANM).
VEATIC: Video-based Emotion and Affect Tracking in Context Dataset
Along with the dataset, we propose a new computer vision task to infer the affect of the selected character via both context and character information in each video frame.
Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation Models
Foundation models have achieved remarkable results in 2D and language tasks like image segmentation, object detection, and visual-language understanding.
Class-Level Confidence Based 3D Semi-Supervised Learning
Firstly, a dynamic thresholding strategy is proposed to utilize more unlabeled data, especially for low learning status classes.
RIS-ADMM: A RIS and ADMM-Based Passive and Sparse Sensing Method With Interference Removal
This letter addresses the passive sensing issue utilizing wireless communication signals and RIS amidst interference from wireless access points (APs).
A RIS-Based Vehicle DOA Estimation Method With Integrated Sensing and Communication System
A novel estimation method is proposed in the scenario with a receiver using only one full-functional channel, where multiple measurements for the DOA estimation are achieved by controlling the reflection matrix (measurement matrix) in the RIS.
Efficient DOA Estimation Method for Reconfigurable Intelligent Surfaces Aided UAV Swarm
Specifically, considering the position perturbation of UAVs, a new atomic norm-based DOA estimation method is proposed, where an atomic norm is defined with the parameter of the position perturbation.
SDOA-Net: An Efficient Deep Learning-Based DOA Estimation Network for Imperfect Array
The estimation of direction of arrival (DOA) is a crucial issue in conventional radar, wireless communication, and integrated sensing and communication (ISAC) systems.
Reconfigurable Intelligent Surface Aided Sparse DOA Estimation Method With Non-ULA
The direction of arrival (DOA) estimation problem is addressed in this letter.
Multimodal Semi-Supervised Learning for 3D Objects
This paper explores how the coherence of different modelities of 3D data (e. g. point cloud, image, and mesh) can be used to improve data efficiency for both 3D classification and retrieval tasks.
Self-Supervised Modality-Invariant and Modality-Specific Feature Learning for 3D Objects
Our proposed novel self-supervised model learns two types of distinct features: modality-invariant features and modality-specific features.
Open Domain Question Answering Using Web Tables
Tables extracted from web documents can be used to directly answer many web search queries.
TableQnA: Answering List Intent Queries With Web Tables
For (ii), we develop novel features to compute structure-aware match and train a machine learning model.
A New Atomic Norm for DOA Estimation With Gain-Phase Errors
The problem of direction of arrival (DOA) estimation has been studied for decades as an essential technology in enabling radar, wireless communications, and array signal processing related applications.
Visualizing Topographic Independent Component Analysis with Movies
Independent component analysis (ICA) has often been used as a tool to model natural image statistics by separating multivariate signals in the image into components that are assumed to be independent.
TRANSRW at SemEval-2018 Task 12: Transforming Semantic Representations for Argument Reasoning Comprehension
The task is to select the correct warrant that explains reasoning of a particular argument consisting of a claim and a reason.
Off-Grid DOA Estimation Using Sparse Bayesian Learning in MIMO Radar With Unknown Mutual Coupling
With regard to the DOA estimation performance, the proposed SBLMC method can outperform state-of-the-art methods in the MIMO radar with unknown mutual coupling effect, while keeping the acceptable computational complexity.
Face Super-Resolution Through Wasserstein GANs
Generative adversarial networks (GANs) have received a tremendous amount of attention in the past few years, and have inspired applications addressing a wide range of problems.
