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Found 7 papers, 4 papers with code
DDMI: Domain-Agnostic Latent Diffusion Models for Synthesizing High-Quality Implicit Neural Representations
Arguably, this architecture limits the expressive power of generative models and results in low-quality INR generation.
Ranked #2 on
Video Generation
on Sky Time-lapse
Explainable Product Classification for Customs
The task of assigning internationally accepted commodity codes (aka HS codes) to traded goods is a critical function of customs offices.
Fine tuning Pre trained Models for Robustness Under Noisy Labels
To tackle this issue, researchers have explored methods for Learning with Noisy Labels to identify clean samples and reduce the influence of noisy labels.
Semantic-Aware Implicit Template Learning via Part Deformation Consistency
In this paper, we highlight the importance of part deformation consistency and propose a semantic-aware implicit template learning framework to enable semantically plausible deformation.
Self-positioning Point-based Transformer for Point Cloud Understanding
In this paper, we present a Self-Positioning point-based Transformer (SPoTr), which is designed to capture both local and global shape contexts with reduced complexity.
Ranked #2 on
3D Part Segmentation
on ShapeNet-Part
Metropolis-Hastings Data Augmentation for Graph Neural Networks
We also propose a simple and effective semi-supervised learning strategy with generated samples from MH-Aug. Our extensive experiments demonstrate that MH-Aug can generate a sequence of samples according to the target distribution to significantly improve the performance of GNNs.
Point Cloud Augmentation with Weighted Local Transformations
Although data augmentation is a standard approach to compensate for the scarcity of data, it has been less explored in the point cloud literature.
Ranked #11 on
Point Cloud Classification
on PointCloud-C
