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Found 9 papers, 4 papers with code
Graph Neural Networks for Learning Equivariant Representations of Neural Networks
Neural networks that process the parameters of other neural networks find applications in domains as diverse as classifying implicit neural representations, generating neural network weights, and predicting generalization errors.
Dynamic Prototype Adaptation with Distillation for Few-shot Point Cloud Segmentation
Existing prototype-based methods rely on support prototypes to guide the segmentation of query point clouds, but they encounter challenges when significant object variations exist between the support prototypes and query features.
Motion Flow Matching for Human Motion Synthesis and Editing
In this paper, we propose \emph{Motion Flow Matching}, a novel generative model designed for human motion generation featuring efficient sampling and effectiveness in motion editing applications.
Guided Diffusion from Self-Supervised Diffusion Features
However, recent studies have revealed that the feature representation derived from diffusion model itself is discriminative for numerous downstream tasks as well, which prompts us to propose a framework to extract guidance from, and specifically for, diffusion models.
Kernel Learning in Ridge Regression "Automatically" Yields Exact Low Rank Solution
Assuming that the covariates have nonzero explanatory power for the response only through a low dimensional subspace (central mean subspace), we find that the global minimizer of the finite sample kernel learning objective is also low rank with high probability.
3D Equivariant Graph Implicit Functions
In this work, we address two key limitations of such representations, in failing to capture local 3D geometric fine details, and to learn from and generalize to shapes with unseen 3D transformations.
Unsharp Mask Guided Filtering
The state-of-the-art leverages deep networks to estimate the two core coefficients of the guided filter.
PointMixup: Augmentation for Point Clouds
In this paper, we define data augmentation between point clouds as a shortest path linear interpolation.
Ranked #3 on
3D Point Cloud Data Augmentation
on ModelNet40
3D Point Cloud Classification
3D Point Cloud Data Augmentation
+2
3D Neighborhood Convolution: Learning Depth-Aware Features for RGB-D and RGB Semantic Segmentation
We propose to model the effective receptive field of 2D convolution based on the scale and locality from the 3D neighborhood.
