Search Results for author: Nataniel Ruiz

Found 15 papers, 5 papers with code

Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing

no code implementations29 Nov 2022 Nataniel Ruiz, Sarah Adel Bargal, Cihang Xie, Kate Saenko, Stan Sclaroff

One shortcoming of this is the fact that these deep neural networks cannot be easily evaluated for robustness issues with respect to specific scene variations.

Human Body Measurement Estimation with Adversarial Augmentation

no code implementations11 Oct 2022 Nataniel Ruiz, Miriam Bellver, Timo Bolkart, Ambuj Arora, Ming C. Lin, Javier Romero, Raja Bala

Training of BMnet is performed on data from real human subjects, and augmented with a novel adversarial body simulator (ABS) that finds and synthesizes challenging body shapes.

DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation

4 code implementations25 Aug 2022 Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, Kfir Aberman

Once the subject is embedded in the output domain of the model, the unique identifier can then be used to synthesize fully-novel photorealistic images of the subject contextualized in different scenes.

Image Generation

Examining the Human Perceptibility of Black-Box Adversarial Attacks on Face Recognition

no code implementations ICML Workshop AML 2021 Benjamin Spetter-Goldstein, Nataniel Ruiz, Sarah Adel Bargal

We also show how the $\ell_2$ norm and other metrics do not correlate with human perceptibility in a linear fashion, thus making these norms suboptimal at measuring adversarial attack perceptibility.

Adversarial Attack Face Recognition

Simulated Adversarial Testing of Face Recognition Models

no code implementations CVPR 2022 Nataniel Ruiz, Adam Kortylewski, Weichao Qiu, Cihang Xie, Sarah Adel Bargal, Alan Yuille, Stan Sclaroff

In this work, we propose a framework for learning how to test machine learning algorithms using simulators in an adversarial manner in order to find weaknesses in the model before deploying it in critical scenarios.

BIG-bench Machine Learning Face Recognition

MorphGAN: One-Shot Face Synthesis GAN for Detecting Recognition Bias

no code implementations9 Dec 2020 Nataniel Ruiz, Barry-John Theobald, Anurag Ranjan, Ahmed Hussein Abdelaziz, Nicholas Apostoloff

Images generated using MorphGAN conserve the identity of the person in the original image, and the provided control over head pose and facial expression allows test sets to be created to identify robustness issues of a facial recognition deep network with respect to pose and expression.

Data Augmentation Face Generation +1

Learning To Simulate

no code implementations ICLR 2019 Nataniel Ruiz, Samuel Schulter, Manmohan Chandraker

Simulation is a useful tool in situations where training data for machine learning models is costly to annotate or even hard to acquire.

Learning to Localize and Align Fine-Grained Actions to Sparse Instructions

no code implementations22 Sep 2018 Meera Hahn, Nataniel Ruiz, Jean-Baptiste Alayrac, Ivan Laptev, James M. Rehg

Automatic generation of textual video descriptions that are time-aligned with video content is a long-standing goal in computer vision.

Object Recognition

Fine-Grained Head Pose Estimation Without Keypoints

9 code implementations2 Oct 2017 Nataniel Ruiz, Eunji Chong, James M. Rehg

Estimating the head pose of a person is a crucial problem that has a large amount of applications such as aiding in gaze estimation, modeling attention, fitting 3D models to video and performing face alignment.

Face Alignment Gaze Estimation +1

Dockerface: an Easy to Install and Use Faster R-CNN Face Detector in a Docker Container

1 code implementation15 Aug 2017 Nataniel Ruiz, James M. Rehg

Face detection is a very important task and a necessary pre-processing step for many applications such as facial landmark detection, pose estimation, sentiment analysis and face recognition.

Face Detection Face Recognition +3

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