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Found 8 papers, 1 papers with code
Towards a Novel Perspective on Adversarial Examples Driven by Frequency
In this paper, we seek to demystify this relationship by exploring the characteristics of adversarial perturbations within the frequency domain.
DreamScene360: Unconstrained Text-to-3D Scene Generation with Panoramic Gaussian Splatting
This point cloud serves as the initial state for the centroids of 3D Gaussians.
Enhancing Tracking Robustness with Auxiliary Adversarial Defense Networks
Moreover, it can be seamlessly integrated with other visual trackers as a plug-and-play module without requiring any parameter adjustments.
Discriminative Adversarial Unlearning
We consider the scenario of two networks, the attacker $\mathbf{A}$ and the trained defender $\mathbf{D}$ pitted against each other in an adversarial objective, wherein the attacker aims at teasing out the information of the data to be unlearned in order to infer membership, and the defender unlearns to defend the network against the attack, whilst preserving its general performance.
Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fields
In this work, we go one step further: in addition to radiance field rendering, we enable 3D Gaussian splatting on arbitrary-dimension semantic features via 2D foundation model distillation.
ALTO: Alternating Latent Topologies for Implicit 3D Reconstruction
One school of thought is to encode a latent vector for each point (point latents).
Link Prediction on Heterophilic Graphs via Disentangled Representation Learning
Therefore, in this paper, we study a novel problem of exploring disentangled representation learning for link prediction on heterophilic graphs.
Label-Wise Graph Convolutional Network for Heterophilic Graphs
Graph Neural Networks (GNNs) have achieved remarkable performance in modeling graphs for various applications.
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