Search Results for author:
Found 6 papers, 4 papers with code
HiLo: Detailed and Robust 3D Clothed Human Reconstruction with High-and Low-Frequency Information of Parametric Models
We empirically find that the high-frequency (HF) and low-frequency (LF) information from a parametric model has the potential to enhance geometry details and improve robustness to noise, respectively.
Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean Discrepancy
Unfortunately, it is challenging to distinguish MGTs and human-written texts because the distributional discrepancy between them is often very subtle due to the remarkable performance of LLMs.
Cross-Ray Neural Radiance Fields for Novel-view Synthesis from Unconstrained Image Collections
To mimic the perception process of humans, in this paper, we propose Cross-Ray NeRF (CR-NeRF) that leverages interactive information across multiple rays to synthesize occlusion-free novel views with the same appearances as the images.
Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score
Last, we propose an EPS-based adversarial detection (EPS-AD) method, in which we develop EPS-based maximum mean discrepancy (MMD) as a metric to measure the discrepancy between the test sample and natural samples.
Towards Efficient Task-Driven Model Reprogramming with Foundation Models
Moreover, the data used for pretraining foundation models are usually invisible and very different from the target data of downstream tasks.
Internal Wasserstein Distance for Adversarial Attack and Defense
Existing attack methods often construct adversarial examples relying on some metrics like the $\ell_p$ distance to perturb samples.
