Publications of Damien Teney sorted by first author
A
| Vision-Language Pretraining: Current Trends and the Future, , and , in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts, 2022 |
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B
| Idiap Scientific Report 2022, , , , , , , , , , , , , , , , , and , Idiap-RR-05-2023 |
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D
| Zero-shot Retrieval: Augmenting Pre-trained Models with Search Engines, , , , , , and , in: Under review, 2023 |
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| Beyond question-based biases: Assessing multimodal shortcut learning in visual question answering, , , and , in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021 |
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| Mysteries of the Deep: Role of Intermediate Representations in Out of Distribution Detection, , , , and , in: Advances in neural information processing systems, 2025 |
| Bayesian low-rank learning (Bella): A practical approach to bayesian neural networks, , , , , , , and , in: Proceedings of the AAAI Conference on Artificial Intelligence, 2025 |
F
| Fine-Tuning Pretrained Models with NVIB for Improved Generalisation, , , , , , , , , , and , in: Workshop on Spurious Correlation and Shortcut Learning: Foundations and Solutions, 2025 |
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H
| SelecMix: Debiased Learning by Contradicting-pair Sampling, , , , , , and , in: Advances in Neural Information Processing Systems, 2022 |
| SelecMix: Debiased Learning by Mixing up Contradicting Pairs, , , , , , and , in: ICML Workshop on Spurious Correlations, Invariance and Stability, 2022 |
J
| OOD-Chameleon: Is Algorithm Selection for OOD Generalization Learnable?, and , in: Forty-Second International Conference on Machine Learning, 2025 |
| Can We Learn to Select the Right Algorithm for OOD Generalization?, and , in: Out Of Distribution Generalization in Computer Vision, Workshop at ECCV, 2024 |
L
| CulturePark: Boosting Cross-cultural Understanding in Large Language Models, , , , , and , in: Advances in Neural Information Processing Systems (NeurIPS), 2024 |
| Image Retrieval on Real-life Images with Pre-trained Vision-and-Language Models, , , and , in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021 |
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| Bi-directional Training for Composed Image Retrieval via Text Prompt Learning, , , , and , in: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024 |
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| Candidate Set Re-ranking for Composed Image Retrieval with Dual Multi-modal Encoder, , , and , in: Transactions on Machine Learning Research (TMLR), 2024 |
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| ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning, , , , , , , , and , in: NeurIPS 2024 Workshop on Federated Learning, 2024 |
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M
| A Symbolic Framework for Systematic Evaluation of Mathematical Reasoning with Transformers, , , and , in: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024 |
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N
| Learning diverse features in vision transformers for improved generalization, , , and , in: ICML 2023: The Second Workshop on Spurious Correlations, Invariance and Stability, 2023 |
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P
| AugGen: Synthetic Augmentation using Diffusion Models Can Improve Recognition, , and , in: The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025 |
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| Active Learning by Feature Mixing, , , , , and , in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022 |
R
| Synergy and diversity in CLIP: Enhancing performance through adaptive backbone ensembling, , , , , and , in: International Conference on Learning Representations, 2025 |
S
| Leveraging Diffusion Disentangled Representations to Mitigate Shortcuts in Underspecified Visual Tasks, , , , and , in: NeurIPS Workshop on Diffusion Models, 2023 |
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| Shortcut Bias Mitigation via Ensemble Diversity Using Diffusion Probabilistic Models, , , , , and , in: Under review, 2023 |
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| EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering, , , , and , in: arXiv, 2022 |
| Reasoning over vision and language: Exploring the benefits of supplemental knowledge, , , and , in: arXiv, 2022 |
| Transformers Pretrained on Procedural Data Contain Modular Structures for Algorithmic Reasoning, , , , and , in: ICML 2025 Workshop on Methods and Opportunities at Small Scale, 2025 |
T
| Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalization, , , and , in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022 |
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| Predicting is not understanding: Recognizing and addressing underspecification in machine learning, , and , in: European Conference on Computer Vision, pages 458-476, Springer, 2022 |
| Unshuffling data for improved generalization in visual question answering, , and , in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021 |
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| Do We Always Need the Simplicity Bias? Looking for Optimal Inductive Biases in the Wild, , , and , in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025 |
| ID and OOD performance are sometimes inversely correlated on real-world datasets, , , and , in: Advances in Neural Information Processing Systems (NeurIPS), 2023 |
| Neural Redshift: Random Networks are not Random Functions, , , and , in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 |
| Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup, , and , in: International Conference on Machine Learning (ICML), 2024 |
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