36 citations · 55 across the 9 of their papers we have counts for
9 papers
Weight Space Representation Learning via Neural Field Adaptation
Zhuoqian Yang, Mathieu Salzmann, Sabine Süsstrunk
We investigate the potential of weights to serve as effective representations, focusing on neural fields. Our key insight is that constraining the optimization space through a pre-…
Dark Noise Diffusion: Noise Synthesis for Low-Light Image Denoising
Liying Lu, Raphaël Achddou, Sabine Süsstrunk
Low-light photography produces images with low signal-to-noise ratios due to limited photons. In such conditions, common approximations like the Gaussian noise model fall short, an…
Exploiting the Signal-Leak Bias in Diffusion Models
Martin Nicolas Everaert, Athanasios Fitsios, Marco Bocchio +3
There is a bias in the inference pipeline of most diffusion models. This bias arises from a signal leak whose distribution deviates from the noise distribution, creating a discrepa…
InNeRF360: Text-Guided 3D-Consistent Object Inpainting on 360-degree Neural Radiance Fields
Dongqing Wang, Tong Zhang, Alaa Abboud +1
We propose InNeRF360, an automatic system that accurately removes text-specified objects from 360-degree Neural Radiance Fields (NeRF). The challenge is to effectively remove objec…
Spatiotemporal Self-supervised Learning for Point Clouds in the Wild
Yanhao Wu, Tong Zhang, Wei Ke +2
Self-supervised learning (SSL) has the potential to benefit many applications, particularly those where manually annotating data is cumbersome. One such situation is the semantic s…
VolRecon: Volume Rendering of Signed Ray Distance Functions for Generalizable Multi-View Reconstruction
Yufan Ren, Fangjinhua Wang, Tong Zhang +2
The success of the Neural Radiance Fields (NeRF) in novel view synthesis has inspired researchers to propose neural implicit scene reconstruction. However, most existing neural imp…
AIM 2020: Scene Relighting and Illumination Estimation Challenge
Majed El Helou, Ruofan Zhou, Sabine Süsstrunk +34
We review the AIM 2020 challenge on virtual image relighting and illumination estimation. This paper presents the novel VIDIT dataset used in the challenge and the different propos…
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them
Chen Liu, Mathieu Salzmann, Tao Lin +2
We analyze the influence of adversarial training on the loss landscape of machine learning models. To this end, we first provide analytical studies of the properties of adversarial…
Training Provably Robust Models by Polyhedral Envelope Regularization
Chen Liu, Mathieu Salzmann, Sabine Süsstrunk
Training certifiable neural networks enables one to obtain models with robustness guarantees against adversarial attacks. In this work, we introduce a framework to bound the advers…