Search Results for author: Sarah Adel Bargal

Found 26 papers, 13 papers with code

Lasagna: Layered Score Distillation for Disentangled Object Relighting

1 code implementation30 Nov 2023 Dina Bashkirova, Arijit Ray, Rupayan Mallick, Sarah Adel Bargal, Jianming Zhang, Ranjay Krishna, Kate Saenko

Although generative editing methods now enable some forms of image editing, relighting is still beyond today's capabilities; existing methods struggle to keep other aspects of the image -- colors, shapes, and textures -- consistent after the edit.

Colorization Object +1

Hardwiring ViT Patch Selectivity into CNNs using Patch Mixing

no code implementations30 Jun 2023 Ariel N. Lee, Sarah Adel Bargal, Janavi Kasera, Stan Sclaroff, Kate Saenko, Nataniel Ruiz

We hypothesize that this power to ignore out-of-context information (which we name $\textit{patch selectivity}$), while integrating in-context information in a non-local manner in early layers, allows ViTs to more easily handle occlusion.

Data Augmentation Inductive Bias

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.

counterfactual Object

Temporal Relevance Analysis for Video Action Models

no code implementations25 Apr 2022 Quanfu Fan, Donghyun Kim, Chun-Fu, Chen, Stan Sclaroff, Kate Saenko, Sarah Adel Bargal

In this paper, we provide a deep analysis of temporal modeling for action recognition, an important but underexplored problem in the literature.

Action Recognition

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

ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes

1 code implementation CVPR 2022 Dina Bashkirova, Mohamed Abdelfattah, Ziliang Zhu, James Akl, Fadi Alladkani, Ping Hu, Vitaly Ablavsky, Berk Calli, Sarah Adel Bargal, Kate Saenko

Recyclable waste detection poses a unique computer vision challenge as it requires detection of highly deformable and often translucent objects in cluttered scenes without the kind of context information usually present in human-centric datasets.

Object object-detection +5

NBDT: Neural-Backed Decision Tree

no code implementations ICLR 2021 Alvin Wan, Lisa Dunlap, Daniel Ho, Jihan Yin, Scott Lee, Suzanne Petryk, Sarah Adel Bargal, Joseph E. Gonzalez

Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use.

NBDT: Neural-Backed Decision Trees

2 code implementations1 Apr 2020 Alvin Wan, Lisa Dunlap, Daniel Ho, Jihan Yin, Scott Lee, Henry Jin, Suzanne Petryk, Sarah Adel Bargal, Joseph E. Gonzalez

Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use.

DMCL: Distillation Multiple Choice Learning for Multimodal Action Recognition

1 code implementation23 Dec 2019 Nuno C. Garcia, Sarah Adel Bargal, Vitaly Ablavsky, Pietro Morerio, Vittorio Murino, Stan Sclaroff

In this work, we address the problem of learning an ensemble of specialist networks using multimodal data, while considering the realistic and challenging scenario of possible missing modalities at test time.

Action Recognition Multiple-choice +1

Excitation Dropout: Encouraging Plasticity in Deep Neural Networks

1 code implementation23 May 2018 Andrea Zunino, Sarah Adel Bargal, Pietro Morerio, Jianming Zhang, Stan Sclaroff, Vittorio Murino

In this work, we utilize the evidence at each neuron to determine the probability of dropout, rather than dropping out neurons uniformly at random as in standard dropout.

Decision Making Video Recognition

Hashing with Mutual Information

2 code implementations2 Mar 2018 Fatih Cakir, Kun He, Sarah Adel Bargal, Stan Sclaroff

Binary vector embeddings enable fast nearest neighbor retrieval in large databases of high-dimensional objects, and play an important role in many practical applications, such as image and video retrieval.

Image Retrieval Retrieval +1

Excitation Backprop for RNNs

1 code implementation CVPR 2018 Sarah Adel Bargal, Andrea Zunino, Donghyun Kim, Jianming Zhang, Vittorio Murino, Stan Sclaroff

Models are trained to caption or classify activity in videos, but little is known about the evidence used to make such decisions.

Action Recognition Temporal Action Localization +1

MIHash: Online Hashing with Mutual Information

1 code implementation ICCV 2017 Fatih Cakir, Kun He, Sarah Adel Bargal, Stan Sclaroff

Learning-based hashing methods are widely used for nearest neighbor retrieval, and recently, online hashing methods have demonstrated good performance-complexity trade-offs by learning hash functions from streaming data.

Image Retrieval Retrieval

Online Supervised Hashing for Ever-Growing Datasets

no code implementations10 Nov 2015 Fatih Cakir, Sarah Adel Bargal, Stan Sclaroff

To address these issues, we propose an online hashing method that is amenable to changes and expansions of the datasets.

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