Search Results for author: Caner Hazirbas

Found 17 papers, 6 papers with code

The Bias of Harmful Label Associations in Vision-Language Models

no code implementations11 Feb 2024 Caner Hazirbas, Alicia Sun, Yonathan Efroni, Mark Ibrahim

Despite the remarkable performance of foundation vision-language models, the shared representation space for text and vision can also encode harmful label associations detrimental to fairness.

Fairness

VPA: Fully Test-Time Visual Prompt Adaptation

no code implementations26 Sep 2023 Jiachen Sun, Mark Ibrahim, Melissa Hall, Ivan Evtimov, Z. Morley Mao, Cristian Canton Ferrer, Caner Hazirbas

Inspired by the success of textual prompting, several studies have investigated the efficacy of visual prompt tuning.

Pseudo Label Test-time Adaptation +3

Pinpointing Why Object Recognition Performance Degrades Across Income Levels and Geographies

1 code implementation11 Apr 2023 Laura Gustafson, Megan Richards, Melissa Hall, Caner Hazirbas, Diane Bouchacourt, Mark Ibrahim

As an example, we show that mitigating a model's vulnerability to texture can improve performance on the lower income level.

Object Recognition

The Casual Conversations v2 Dataset

no code implementations8 Mar 2023 Bilal Porgali, Vítor Albiero, Jordan Ryda, Cristian Canton Ferrer, Caner Hazirbas

This paper introduces a new large consent-driven dataset aimed at assisting in the evaluation of algorithmic bias and robustness of computer vision and audio speech models in regards to 11 attributes that are self-provided or labeled by trained annotators.

Fairness

A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others

1 code implementation CVPR 2023 Zhiheng Li, Ivan Evtimov, Albert Gordo, Caner Hazirbas, Tal Hassner, Cristian Canton Ferrer, Chenliang Xu, Mark Ibrahim

Key to advancing the reliability of vision systems is understanding whether existing methods can overcome multiple shortcuts or struggle in a Whac-A-Mole game, i. e., where mitigating one shortcut amplifies reliance on others.

Domain Generalization Image Classification +1

ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations

no code implementations3 Nov 2022 Badr Youbi Idrissi, Diane Bouchacourt, Randall Balestriero, Ivan Evtimov, Caner Hazirbas, Nicolas Ballas, Pascal Vincent, Michal Drozdzal, David Lopez-Paz, Mark Ibrahim

Equipped with ImageNet-X, we investigate 2, 200 current recognition models and study the types of mistakes as a function of model's (1) architecture, e. g. transformer vs. convolutional, (2) learning paradigm, e. g. supervised vs. self-supervised, and (3) training procedures, e. g., data augmentation.

Data Augmentation

Fairness Indicators for Systematic Assessments of Visual Feature Extractors

1 code implementation15 Feb 2022 Priya Goyal, Adriana Romero Soriano, Caner Hazirbas, Levent Sagun, Nicolas Usunier

Systematic diagnosis of fairness, harms, and biases of computer vision systems is an important step towards building socially responsible systems.

Fairness

Towards Measuring Fairness in Speech Recognition: Casual Conversations Dataset Transcriptions

no code implementations18 Nov 2021 Chunxi Liu, Michael Picheny, Leda Sari, Pooja Chitkara, Alex Xiao, Xiaohui Zhang, Mark Chou, Andres Alvarado, Caner Hazirbas, Yatharth Saraf

This paper presents initial Speech Recognition results on "Casual Conversations" -- a publicly released 846 hour corpus designed to help researchers evaluate their computer vision and audio models for accuracy across a diverse set of metadata, including age, gender, and skin tone.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Localized Uncertainty Attacks

no code implementations17 Jun 2021 Ousmane Amadou Dia, Theofanis Karaletsos, Caner Hazirbas, Cristian Canton Ferrer, Ilknur Kaynar Kabul, Erik Meijer

Under this threat model, we create adversarial examples by perturbing only regions in the inputs where a classifier is uncertain.

What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?

1 code implementation19 Jan 2018 Nikolaus Mayer, Eddy Ilg, Philipp Fischer, Caner Hazirbas, Daniel Cremers, Alexey Dosovitskiy, Thomas Brox

The finding that very large networks can be trained efficiently and reliably has led to a paradigm shift in computer vision from engineered solutions to learning formulations.

Optical Flow Estimation

Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems

1 code implementation ICCV 2017 Tim Meinhardt, Michael Moeller, Caner Hazirbas, Daniel Cremers

While variational methods have been among the most powerful tools for solving linear inverse problems in imaging, deep (convolutional) neural networks have recently taken the lead in many challenging benchmarks.

Demosaicking Denoising +1

Deep Depth From Focus

5 code implementations4 Apr 2017 Caner Hazirbas, Sebastian Georg Soyer, Maximilian Christian Staab, Laura Leal-Taixé, Daniel Cremers

Depth from focus (DFF) is one of the classical ill-posed inverse problems in computer vision.

Depth Estimation

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