604 citations · 1.1k across the 19 of their papers we have counts for
19 papers
UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling
Haider Al-Tahan, Quentin Garrido, Randall Balestriero +3
Significant research efforts have been made to scale and improve vision-language model (VLM) training approaches. Yet, with an ever-growing number of benchmarks, researchers are ta…
The Bias of Harmful Label Associations in Vision-Language Models
Caner Hazirbas, Alicia Sun, Yonathan Efroni +1
Despite the remarkable performance of foundation vision-language models, the shared representation space for text and vision can also encode harmful label associations detrimental…
VPA: Fully Test-Time Visual Prompt Adaptation
Jiachen Sun, Mark Ibrahim, Melissa Hall +4
Textual prompt tuning has demonstrated significant performance improvements in adapting natural language processing models to a variety of downstream tasks by treating hand-enginee…
Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis
Maxim Maximov, Tim Meinhardt, Ismail Elezi +4
The advent of data-driven technology solutions is accompanied by an increasing concern with data privacy. This is of particular importance for human-centered image recognition task…
Pinpointing Why Object Recognition Performance Degrades Across Income Levels and Geographies
Laura Gustafson, Megan Richards, Melissa Hall +3
Despite impressive advances in object-recognition, deep learning systems' performance degrades significantly across geographies and lower income levels raising pressing concerns of…
The Casual Conversations v2 Dataset
Bilal Porgali, Vítor Albiero, Jordan Ryda +2
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 regar…
A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others
Zhiheng Li, Ivan Evtimov, Albert Gordo +5
Machine learning models have been found to learn shortcuts -- unintended decision rules that are unable to generalize -- undermining models' reliability. Previous works address thi…
Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustness
Caner Hazirbas, Yejin Bang, Tiezheng Yu +9
Developing robust and fair AI systems require datasets with comprehensive set of labels that can help ensure the validity and legitimacy of relevant measurements. Recent efforts, t…
ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations
Badr Youbi Idrissi, Diane Bouchacourt, Randall Balestriero +7
Deep learning vision systems are widely deployed across applications where reliability is critical. However, even today's best models can fail to recognize an object when its pose,…
Generating High Fidelity Data from Low-density Regions using Diffusion Models
Vikash Sehwag, Caner Hazirbas, Albert Gordo +2
Our work focuses on addressing sample deficiency from low-density regions of data manifold in common image datasets. We leverage diffusion process based generative models to synthe…
Fairness Indicators for Systematic Assessments of Visual Feature Extractors
Priya Goyal, Adriana Romero Soriano, Caner Hazirbas +2
Does everyone equally benefit from computer vision systems? Answers to this question become more and more important as computer vision systems are deployed at large scale, and can…
Towards Measuring Fairness in Speech Recognition: Casual Conversations Dataset Transcriptions
Chunxi Liu, Michael Picheny, Leda Sarı +7
It is well known that many machine learning systems demonstrate bias towards specific groups of individuals. This problem has been studied extensively in the Facial Recognition are…
Localized Uncertainty Attacks
Ousmane Amadou Dia, Theofanis Karaletsos, Caner Hazirbas +3
The susceptibility of deep learning models to adversarial perturbations has stirred renewed attention in adversarial examples resulting in a number of attacks. However, most of the…
Towards Measuring Fairness in AI: the Casual Conversations Dataset
Caner Hazirbas, Joanna Bitton, Brian Dolhansky +3
This paper introduces a novel dataset to help researchers evaluate their computer vision and audio models for accuracy across a diverse set of age, genders, apparent skin tones and…
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
Nikolaus Mayer, Eddy Ilg, Philipp Fischer +4
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. As a…
Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems
Tim Meinhardt, Michael Moeller, Caner Hazirbas +1
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…
Deep Depth From Focus
Caner Hazirbas, Sebastian Georg Soyer, Maximilian Christian Staab +2
Depth from focus (DFF) is one of the classical ill-posed inverse problems in computer vision. Most approaches recover the depth at each pixel based on the focal setting which exhib…
Image-based localization using LSTMs for structured feature correlation
Florian Walch, Caner Hazirbas, Laura Leal-Taixé +3
In this work we propose a new CNN+LSTM architecture for camera pose regression for indoor and outdoor scenes. CNNs allow us to learn suitable feature representations for localizati…
FlowNet: Learning Optical Flow with Convolutional Networks
Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg +6
Convolutional neural networks (CNNs) have recently been very successful in a variety of computer vision tasks, especially on those linked to recognition. Optical flow estimation ha…