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20192025
most citedOn the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them

36 citations · 55 across the 9 of their papers we have counts for

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6 papers · 1 filter

cs.CV2025

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…

cs.CV2023

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…

cs.CV2023★ 4 cited

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…

cs.CV2023

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…

cs.CV2022★ 1 cited

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…

cs.CV2020★ 14 cited

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…