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Sabine Süsstrunk

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Publications associées (188)

Training Provably Robust Models by Polyhedral Envelope Regularization

Mathieu Salzmann, Sabine Süsstrunk, Chen Liu

Training certifiable neural networks enables us to obtain models with robustness guarantees against adversarial attacks. In this work, we introduce a framework to obtain a provable adversarial-free region in the neighborhood of the input data by a polyhedr ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration

Sabine Süsstrunk, Majed El Helou

Classic image-restoration algorithms use a variety of priors, either implicitly or explicitly. Their priors are hand-designed and their corresponding weights are heuristically assigned. Hence, deep learning methods often produce superior image restoration ...

2022

Leverage Your Local and Global Representations: A New Self-Supervised Learning Strategy

Mathieu Salzmann, Sabine Süsstrunk, Tong Zhang

Self-supervised learning (SSL) methods aim to learn view-invariant representations by maximizing the similarity between the features extracted from different crops of the same image regardless of cropping size and content. In essence, this strategy ignores ...

IEEE COMPUTER SOC2022

Modeling Object Dissimilarity for Deep Saliency Prediction

Mathieu Salzmann, Sabine Süsstrunk, Bahar Aydemir, Deblina Bhattacharjee, Seungryong Kim, Tong Zhang

Saliency prediction has made great strides over the past two decades, with current techniques modeling low-level information, such as color, intensity and size contrasts, and high-level ones, such as attention and gaze direction for entire objects. Despite ...

2022

RC-MVSNet: Unsupervised Multi-View Stereo with Neural Rendering

Sabine Süsstrunk, Tong Zhang, Di Chang

Finding accurate correspondences among different views is the Achilles’ heel of unsupervised Multi-View Stereo (MVS). Existing methods are built upon the assumption that corresponding pixels share similar photometric features. However, multi-view images in ...

Springer2022

MulT: An End-to-End Multitask Learning Transformer

Mathieu Salzmann, Sabine Süsstrunk, Deblina Bhattacharjee, Tong Zhang

We propose an end-to-end Multitask Learning Transformer framework, named MulT, to simultaneously learn multiple high-level vision tasks, including depth estimation, semantic segmentation, reshading, surface normal estimation, 2D keypoint detection, and edg ...

IEEE COMPUTER SOC2022

Robust Binary Models by Pruning Randomly-initialized Networks

Mathieu Salzmann, Sabine Süsstrunk, Chen Liu, Ziqi Zhao

Robustness to adversarial attacks was shown to require a larger model capacity, and thus a larger memory footprint. In this paper, we introduce an approach to obtain robust yet compact models by pruning randomly-initialized binary networks. Unlike adversar ...

2022

PoGaIN: Poisson-Gaussian Image Noise Modeling From Paired Samples

Sabine Süsstrunk, Majed El Helou, Kaan Okumus

Image noise can often be accurately fitted to a Poisson-Gaussian distribution. However, estimating the distribution parameters from a noisy image only is a challenging task. Here, we study the case when paired noisy and noise-free samples are accessible. N ...

IEEE Institute of Electrical and Electronics Engineers2022

Image Denoising with Control over Deep Network Hallucination

Sabine Süsstrunk, Majed El Helou, Qiyuan Liang

Deep image denoisers achieve state-of-the-art results but with a hidden cost. As witnessed in recent literature, these deep networks are capable of overfitting their training distributions, causing inaccurate hallucinations to be added to the output and ge ...

Society for Imaging Science and Technology (IS&T)2022

Estimating Image Depth in the Comics Domain

Mathieu Salzmann, Sabine Süsstrunk, Deblina Bhattacharjee, Martin Nicolas Everaert

Estimating the depth of comics images is challenging as such images a) are monocular; b) lack ground-truth depth annotations; c) differ across different artistic styles; d) are sparse and noisy. We thus, use an off-the-shelf unsupervised image to image tra ...

2021

Adaptive extreme video completion

Sabine Süsstrunk, Radhakrishna Achanta, Ruofan Zhou, Majed El Helou

A computer-implemented video completion method is proposed for reconstructing a sparsely sampled video comprising sparsely sampled video frames comprising sparse sets of picture element intensity values. The method comprises: distributing the sparse sets o ...

2021

Fidelity Estimation Improves Noisy-Image Classification with Pretrained Networks

Sabine Süsstrunk, Majed El Helou, Deblina Bhattacharjee, Xiaoyu Lin

Image classification has significantly improved using deep learning. This is mainly due to convolutional neural networks (CNNs) that are capable of learning rich feature extractors from large datasets. However, most deep learning classification methods are ...

IEEE Institute of Electrical and Electronics Engineers2021

Deep Gaussian denoiser epistemic uncertainty and decoupled dual-attention fusion

Sabine Süsstrunk, Majed El Helou, Xiaoyu Lin, Xiaoqi Ma

Following the performance breakthrough of denoising networks, improvements have come chiefly through novel architecture designs and increased depth. While novel denoising networks were designed for real images coming from different distributions, or for sp ...

2021

Optimizing Latent Space Directions For GAN-based Local Image Editing

Sabine Süsstrunk, Ehsan Pajouheshgar, Tong Zhang

Generative Adversarial Network (GAN) based localized image editing can suffer from ambiguity between semantic attributes. We thus present a novel objective function to evaluate the locality of an image edit. By introducing the supervision from a pre-traine ...

IEEE2021

On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them

Mathieu Salzmann, Sabine Süsstrunk, Tao Lin, Chen Liu

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 loss functions under different adversarial budgets. We then demonstrate tha ...

2021

NTIRE 2021 Depth Guided Image Relighting Challenge

Sabine Süsstrunk, Radu Timofte, Ruofan Zhou, Majed El Helou

Image relighting is attracting increasing interest due to its various applications. From a research perspective, image relighting can be exploited to conduct both image normalization for domain adaptation, and also for data augmentation. It also has multip ...

IEEE COMPUTER SOC2021

Editing in Style: Uncovering the Local Semantics of GANs

Sabine Süsstrunk, Edo Collins

While the quality of GAN image synthesis has improved tremendously in recent years, our ability to control and condition the output is still limited. Focusing on StyleGAN, we introduce a simple and effective method for making local, semantically-aware edit ...

IEEE2020

2d-3d sculpture paintings

Roger Hersch, Sabine Süsstrunk, Mahmut Sami Arpa

We disclose a method for synthesizing by computer an artwork where 2D and 3D contents are integrated within a same composition. Such an artwork creates plausible effects for the viewers by showing a different relationship between 2D and 3D at each viewing ...

2020

Dynamic media content categorisation method

Sabine Süsstrunk, Gabriel Albert Autes, Mahmut Sami Arpa

The present invention concerns a method of classifying a media item into a user profile suitability class in a classification system comprising a set of pre-trained artificial neural networks. The method comprising: receiving (101) a media file; extracting ...

2020

Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks

Sabine Süsstrunk, Ruofan Zhou, Majed El Helou

Super-resolution and denoising are ill-posed yet fundamental image restoration tasks. In blind settings, the degradation kernel or the noise level are unknown. This makes restoration even more challenging, notably for learning-based methods, as they tend t ...

2020

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