5 citations · 12 across the 12 of their papers we have counts for
16 papers
Scaling Sequence-to-Sequence Generative Neural Rendering
Shikun Liu, Kam Woh Ng, Wonbong Jang +9
We present Kaleido, a family of generative models designed for photorealistic, unified object- and scene-level neural rendering. Kaleido operates on the principle that 3D can be re…
MarDini: Masked Autoregressive Diffusion for Video Generation at Scale
Haozhe Liu, Shikun Liu, Zijian Zhou +12
We introduce MarDini, a new family of video diffusion models that integrate the advantages of masked auto-regression (MAR) into a unified diffusion model (DM) framework. Here, MAR…
Compressed-Language Models for Understanding Compressed File Formats: a JPEG Exploration
Juan C. Pérez, Alejandro Pardo, Mattia Soldan +3
This study investigates whether Compressed-Language Models (CLMs), i.e. language models operating on raw byte streams from Compressed File Formats~(CFFs), can understand files comp…
Adaptive Guidance: Training-free Acceleration of Conditional Diffusion Models
Angela Castillo, Jonas Kohler, Juan C. Pérez +5
This paper presents a comprehensive study on the role of Classifier-Free Guidance (CFG) in text-conditioned diffusion models from the perspective of inference efficiency. In partic…
Enhancing Neural Rendering Methods with Image Augmentations
Juan C. Pérez, Sara Rojas, Jesus Zarzar +1
Faithfully reconstructing 3D geometry and generating novel views of scenes are critical tasks in 3D computer vision. Despite the widespread use of image augmentations across comput…
Evaluation of Test-Time Adaptation Under Computational Time Constraints
Motasem Alfarra, Hani Itani, Alejandro Pardo +6
This paper proposes a novel online evaluation protocol for Test Time Adaptation (TTA) methods, which penalizes slower methods by providing them with fewer samples for adaptation. T…
Certified Robustness in Federated Learning
Motasem Alfarra, Juan C. Pérez, Egor Shulgin +2
Federated learning has recently gained significant attention and popularity due to its effectiveness in training machine learning models on distributed data privately. However, as…
3DeformRS: Certifying Spatial Deformations on Point Clouds
Gabriel Pérez S., Juan C. Pérez, Motasem Alfarra +2
3D computer vision models are commonly used in security-critical applications such as autonomous driving and surgical robotics. Emerging concerns over the robustness of these model…
Towards Assessing and Characterizing the Semantic Robustness of Face Recognition
Juan C. Pérez, Motasem Alfarra, Ali Thabet +2
Deep Neural Networks (DNNs) lack robustness against imperceptible perturbations to their input. Face Recognition Models (FRMs) based on DNNs inherit this vulnerability. We propose…
On the Robustness of Quality Measures for GANs
Motasem Alfarra, Juan C. Pérez, Anna Frühstück +3
This work evaluates the robustness of quality measures of generative models such as Inception Score (IS) and Fréchet Inception Distance (FID). Analogous to the vulnerability of dee…
Generalized Real-World Super-Resolution through Adversarial Robustness
Angela Castillo, María Escobar, Juan C. Pérez +4
Real-world Super-Resolution (SR) has been traditionally tackled by first learning a specific degradation model that resembles the noise and corruption artifacts in low-resolution i…
Enhancing Adversarial Robustness via Test-time Transformation Ensembling
Juan C. Pérez, Motasem Alfarra, Guillaume Jeanneret +4
Deep learning models are prone to being fooled by imperceptible perturbations known as adversarial attacks. In this work, we study how equipping models with Test-time Transformatio…
Towards Robust General Medical Image Segmentation
Laura Daza, Juan C. Pérez, Pablo Arbeláez
The reliability of Deep Learning systems depends on their accuracy but also on their robustness against adversarial perturbations to the input data. Several attacks and defenses ha…
Combating Adversaries with Anti-Adversaries
Motasem Alfarra, Juan C. Pérez, Ali Thabet +3
Deep neural networks are vulnerable to small input perturbations known as adversarial attacks. Inspired by the fact that these adversaries are constructed by iteratively minimizing…
Rethinking Clustering for Robustness
Motasem Alfarra, Juan C. Pérez, Adel Bibi +3
This paper studies how encouraging semantically-aligned features during deep neural network training can increase network robustness. Recent works observed that Adversarial Trainin…
Gabor Layers Enhance Network Robustness
Juan C. Pérez, Motasem Alfarra, Guillaume Jeanneret +4
We revisit the benefits of merging classical vision concepts with deep learning models. In particular, we explore the effect on robustness against adversarial attacks of replacing…