FlowNet: Learning Optical Flow with Convolutional Networks. - Alexey Dosovitskiy; Philipp Fischer; Eddy Ilg; Philip Häusser; Caner Hazirbas; Vladimir Golkov; Patrick van der Smagt; Daniel Cremers; Thomas Brox (ICCV, 2015)

Interactive Multi-label Segmentation of RGB-D Images. - Julia Diebold; Nikolaus Demmel; Caner Hazirbas; Michael Möller 001; Daniel Cremers (SSVM, 2015)

Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation. - Caner Hazirbas; Julia Diebold; Daniel Cremers (SSVM, 2015)

FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture. - Caner Hazirbas; Lingni Ma; Csaba Domokos; Daniel Cremers (ACCV (1), 2016)

Image-based Localization with Spatial LSTMs. - Florian Walch; Caner Hazirbas; Laura Leal-Taixé; Torsten Sattler; Sebastian Hilsenbeck; Daniel Cremers (CoRR, 2016)

Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems. - Tim Meinhardt; Michael Möller 001; Caner Hazirbas; Daniel Cremers (ICCV, 2017)

Image-Based Localization Using LSTMs for Structured Feature Correlation. - Florian Walch; Caner Hazirbas; Laura Leal-Taixé; Torsten Sattler; Sebastian Hilsenbeck; Daniel Cremers (ICCV, 2017)

Deep Depth From Focus. - Caner Hazirbas; Laura Leal-Taixé; Daniel Cremers (CoRR, 2017)

Deep Depth from Focus. - Caner Hazirbas; Sebastian Georg Soyer; Maximilian Christian Staab; Laura Leal-Taixé; Daniel Cremers (ACCV (3), 2018)

What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation? - Nikolaus Mayer; Eddy Ilg; Philipp Fischer; Caner Hazirbas; Daniel Cremers; Alexey Dosovitskiy; Thomas Brox (Int. J. Comput. Vis., 2018)

Learning Geometry and Semantics for Deep Image Restoration. - Caner Hazirbas (2019)

Casual Conversations: A Dataset for Measuring Fairness in AI. - Caner Hazirbas; Joanna Bitton; Brian Dolhansky; Jacqueline Pan; Albert Gordo; Cristian Canton-Ferrer (CVPR Workshops, 2021)

Localized Uncertainty Attacks. - Ousmane Amadou Dia; Theofanis Karaletsos; Caner Hazirbas; Cristian Canton-Ferrer; Ilknur Kaynar Kabul; Erik Meijer 001 (CoRR, 2021)

Generating High Fidelity Data from Low-density Regions using Diffusion Models. - Vikash Sehwag; Caner Hazirbas; Albert Gordo; Firat Ozgenel; Cristian Canton-Ferrer (CVPR, 2022)

Fairness Indicators for Systematic Assessments of Visual Feature Extractors. - Priya Goyal; Adriana Romero-Soriano; Caner Hazirbas; Levent Sagun; Nicolas Usunier (FAccT, 2022)

Towards Measuring Fairness in Speech Recognition: Casual Conversations Dataset Transcriptions. - Chunxi Liu; Michael Picheny; Leda Sari; Pooja Chitkara; Alex Xiao; Xiaohui Zhang 007; Mark Chou; Andres Alvarado; Caner Hazirbas; Yatharth Saraf (ICASSP, 2022)

Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustness. - Caner Hazirbas; Yejin Bang; Tiezheng Yu; Parisa Assar; Bilal Porgali; Vítor Albiero; Stefan Hermanek; Jacqueline Pan; Emily McReynolds; Miranda Bogen; Pascale Fung; Cristian Canton-Ferrer (CoRR, 2022)

Towards Measuring Fairness in AI: The Casual Conversations Dataset. - Caner Hazirbas; Joanna Bitton; Brian Dolhansky; Jacqueline Pan; Albert Gordo; Cristian Canton-Ferrer (IEEE Trans. Biom. Behav. Identity Sci., 2022)

A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others. - Zhiheng Li 002; Ivan Evtimov; Albert Gordo; Caner Hazirbas; Tal Hassner; Cristian Canton-Ferrer; Chenliang Xu; Mark Ibrahim (CVPR, 2023)

The Casual Conversations v2 Dataset : A diverse, large benchmark for measuring fairness and robustness in audio/vision/speech models. - Bilal Porgali; Vítor Albiero; Jordan Ryda; Cristian Canton-Ferrer; Caner Hazirbas (CVPR Workshops, 2023)

ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations. - Badr Youbi Idrissi; Diane Bouchacourt; Randall Balestriero; Ivan Evtimov; Caner Hazirbas; Nicolas Ballas; Pascal Vincent; Michal Drozdzal; David Lopez-Paz; Mark Ibrahim (ICLR, 2023)

VPA: Fully Test-Time Visual Prompt Adaptation. - Jiachen Sun; Mark Ibrahim; Melissa Hall; Ivan Evtimov; Z. Morley Mao; Cristian Canton-Ferrer; Caner Hazirbas (ACM Multimedia, 2023)

Exploring Why Object Recognition Performance Degrades Across Income Levels and Geographies with Factor Annotations. - Laura Gustafson; Megan Richards; Melissa Hall; Caner Hazirbas; Diane Bouchacourt; Mark Ibrahim (NeurIPS, 2023)

The Casual Conversations v2 Dataset. - Bilal Porgali; Vítor Albiero; Jordan Ryda; Cristian Canton-Ferrer; Caner Hazirbas (CoRR, 2023)

Pinpointing Why Object Recognition Performance Degrades Across Income Levels and Geographies. - Laura Gustafson; Megan Richards; Melissa Hall; Caner Hazirbas; Diane Bouchacourt; Mark Ibrahim (CoRR, 2023)

Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis. - Maxim Maximov; Tim Meinhardt; Ismail Elezi; Zoe Papakipos; Caner Hazirbas; Cristian Canton-Ferrer; Laura Leal-Taixé (CoRR, 2023)

Data-Driven but Privacy-Conscious: Pedestrian Dataset De-Identification via Full-Body Person Synthesis. - Maxim Maximov; Tim Meinhardt; Zoe Papakipos; Caner Hazirbas; Cristian Canton; Laura Leal-Taixé (FG, 2024)

UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling. - Haider Al-Tahan; Quentin Garrido; Randall Balestriero; Diane Bouchacourt; Caner Hazirbas; Mark Ibrahim (NeurIPS, 2024)

The Bias of Harmful Label Associations in Vision-Language Models. - Caner Hazirbas; Alicia Sun; Yonathan Efroni; Mark Ibrahim (CoRR, 2024)