Search Results for author:
Found 17 papers, 3 papers with code
3D-JEPA: A Joint Embedding Predictive Architecture for 3D Self-Supervised Representation Learning
Overall, 3D-JEPA predicts the representation of target blocks from a context block using the encoder and context-aware decoder architecture.
Hyperbolic Image-and-Pointcloud Contrastive Learning for 3D Classification
3D contrastive representation learning has exhibited remarkable efficacy across various downstream tasks.
Multiple Rotation Averaging with Constrained Reweighting Deep Matrix Factorization
Our method synthesizes the merit of both optimization-based and learning-based methods.
GaussianPU: A Hybrid 2D-3D Upsampling Framework for Enhancing Color Point Clouds via 3D Gaussian Splatting
Extensive experimental results on generating million-level point cloud data validate the effectiveness of our method, substantially improving the quality of colored point clouds and demonstrating significant potential for applications involving large-scale point clouds in autonomous robotics and human-robot interaction scenarios.
Neural Modulation Alteration to Positive and Negative Emotions in Depressed Patients: Insights from fMRI Using Positive/Negative Emotion Atlas
This research enhances our understanding of emotion modulation in depression, with implications for diagnosis and treatment evaluation.
Incremental Multiview Point Cloud Registration with Two-stage Candidate Retrieval
In this paper, we propose an incremental multiview point cloud registration method that progressively registers all scans to a growing meta-shape.
Generative Technology for Human Emotion Recognition: A Scope Review
Finally, the review will outline future research directions, emphasizing the potential of generative models to advance the field of emotion recognition and enhance the emotional intelligence of AI systems.
Data-Driven Min-Max MPC for Linear Systems: Robustness and Adaptation
In this paper, we propose a data-driven min-max model predictive control (MPC) scheme using noisy input-state data for unknown linear time-invariant (LTI) system.
Iterative Feedback Network for Unsupervised Point Cloud Registration
In this paper, we propose a novel Iterative Feedback Network (IFNet) for unsupervised point cloud registration, in which the representation of low-level features is efficiently enriched by rerouting subsequent high-level features.
Cross-Modal Information-Guided Network using Contrastive Learning for Point Cloud Registration
Specifically, we first incorporate the projected images from the point clouds and fuse the cross-modal features using the attention mechanism.
DBDNet:Partial-to-Partial Point Cloud Registration with Dual Branches Decoupling
Point cloud registration plays a crucial role in various computer vision tasks, and usually demands the resolution of partial overlap registration in practice.
Data-Driven Min-Max MPC for Linear Systems
Designing data-driven controllers in the presence of noise is an important research problem, in particular when guarantees on stability, robustness, and constraint satisfaction are desired.
GDBN: a Graph Neural Network Approach to Dynamic Bayesian Network
Identifying causal relations among multi-variate time series is one of the most important elements towards understanding the complex mechanisms underlying the dynamic system.
Linear Data-Driven Economic MPC with Generalized Terminal Constraint
By employing a generalized terminal constraint with artificial equilibrium, the scheme does not require prior knowledge of the optimal equilibrium.
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.
Optimal Synthesis of Opacity-Enforcing Supervisors for Qualitative and Quantitative Specifications
We assume that the system has a "secret" that does not want to be revealed to the intruder.
Opacity Enforcing Supervisory Control using Non-deterministic Supervisors
Compared with the standard deterministic control mechanism, such a non-deterministic control mechanism can enhance the plausible deniability of the controlled system as the online control decision is a random realization and cannot be implicitly inferred from the control policy.
