Search Results for author:
Found 9 papers, 3 papers with code
Local-consistent Transformation Learning for Rotation-invariant Point Cloud Analysis
Equipped with LCRF and RPR, our LocoTrans is capable of learning local-consistent transformation and preserving local geometry, which benefits rotation invariance learning.
CharNeRF: 3D Character Generation from Concept Art
3D modeling holds significant importance in the realms of AR/VR and gaming, allowing for both artistic creativity and practical applications.
Behavioral Refinement via Interpolant-based Policy Diffusion
However, the target policy to be learned is often significantly different from Gaussian and this mismatch can result in poor performance when using a small number of diffusion steps (to improve inference speed) and under limited data.
Revealing Decurve Flows for Generalized Graph Propagation
\textbf{Secondly}, we propose the {\em Continuous Unified Ricci Curvature} (\textbf{CURC}), an extension of celebrated {\em Ollivier-Ricci Curvature} for directed and weighted graphs.
Using Human-like Mechanism to Weaken Effect of Pre-training Weight Bias in Face-Recognition Convolutional Neural Network
In this study, we focused on 4 extensively studied CNNs (AlexNet, VGG11, VGG13, and VGG16) which has been analyzed as human-like models by neuroscientists with ample evidence.
Cross-modal & Cross-domain Learning for Unsupervised LiDAR Semantic Segmentation
In recent years, cross-modal domain adaptation has been studied on the paired 2D image and 3D LiDAR data to ease the labeling costs for 3D LiDAR semantic segmentation (3DLSS) in the target domain.
Empirical Evaluation of ChatGPT on Requirements Information Retrieval Under Zero-Shot Setting
Our evaluation of ChatGPT on requirements IR under zero-shot setting provides preliminary evidence for designing or developing more effective requirements IR methods or tools based on LLMs.
Composed Image Retrieval with Text Feedback via Multi-grained Uncertainty Regularization
The key idea underpinning the proposed method is to integrate fine- and coarse-grained retrieval as matching data points with small and large fluctuations, respectively.
Composed Image Retrieval (CoIR)
Image Retrieval with Multi-Modal Query
+1
Machine learning based iterative learning control for non-repetitive time-varying systems
A machine learning (ML) based nominal model update mechanism, which utilizes the linear regression technique to update the nominal model at each ILC trial only using the current trial information, is proposed for non-repetitive TVSs in order to enhance the ILC performance.
