Caner Hazirbas

Orcid: 0000-0003-1980-5768

Affiliations:
  • Technical University of Munich, Germany (PhD 2019)


According to our database1, Caner Hazirbas authored at least 29 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

Online presence:

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Bibliography

2024
UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling.
CoRR, 2024

The Bias of Harmful Label Associations in Vision-Language Models.
CoRR, 2024

Data-Driven but Privacy-Conscious: Pedestrian Dataset De-Identification via Full-Body Person Synthesis.
Proceedings of the 18th IEEE International Conference on Automatic Face and Gesture Recognition, 2024

2023
Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis.
CoRR, 2023

Pinpointing Why Object Recognition Performance Degrades Across Income Levels and Geographies.
CoRR, 2023

The Casual Conversations v2 Dataset.
CoRR, 2023

Exploring Why Object Recognition Performance Degrades Across Income Levels and Geographies with Factor Annotations.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

VPA: Fully Test-Time Visual Prompt Adaptation.
Proceedings of the 31st ACM International Conference on Multimedia, 2023

ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

The Casual Conversations v2 Dataset : A diverse, large benchmark for measuring fairness and robustness in audio/vision/speech models.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Towards Measuring Fairness in AI: The Casual Conversations Dataset.
IEEE Trans. Biom. Behav. Identity Sci., 2022

Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustness.
CoRR, 2022

Towards Measuring Fairness in Speech Recognition: Casual Conversations Dataset Transcriptions.
Proceedings of the IEEE International Conference on Acoustics, 2022

Fairness Indicators for Systematic Assessments of Visual Feature Extractors.
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Generating High Fidelity Data from Low-density Regions using Diffusion Models.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021
Localized Uncertainty Attacks.
CoRR, 2021

Casual Conversations: A Dataset for Measuring Fairness in AI.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

2019
Learning Geometry and Semantics for Deep Image Restoration.
PhD thesis, 2019

2018
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
Int. J. Comput. Vis., 2018

Deep Depth from Focus.
Proceedings of the Computer Vision - ACCV 2018, 2018

2017
Deep Depth From Focus.
CoRR, 2017

Image-Based Localization Using LSTMs for Structured Feature Correlation.
Proceedings of the IEEE International Conference on Computer Vision, 2017

Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems.
Proceedings of the IEEE International Conference on Computer Vision, 2017

2016
Image-based Localization with Spatial LSTMs.
CoRR, 2016

FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture.
Proceedings of the Computer Vision - ACCV 2016, 2016

2015
Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation.
Proceedings of the Scale Space and Variational Methods in Computer Vision, 2015

Interactive Multi-label Segmentation of RGB-D Images.
Proceedings of the Scale Space and Variational Methods in Computer Vision, 2015

FlowNet: Learning Optical Flow with Convolutional Networks.
Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015


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