Damien Teney

Dr Damien Teney

School of Computer Science and Information Technology

College of Engineering and Information Technology

Damien Teney holds an Adjunct Lecturer position at University of Adelaide, while serving as a Research Scientist at the Idiap Research Institute in Martigny, Switzerland. His research is at the intersection of machine learning, computer vision, and natural language processing. He made notable contributions to the field of vision-and-language models, in particular for the tasks of captioning and visual question answering (VQA). His current work focuses on the scientific understanding of large machine learning models, and on their robustness properties e.g. in out-of-distribution conditions.

Year Citation
2024 Opazo, C. R., Abbasnejad, E., Teney, D., Marrese-Taylor, E., Damirchi, H., & Hengel, A. V. D. (2024). Synergy and Diversity in CLIP: Enhancing Performance Through Adaptive Backbone Ensembling.. CoRR, abs/2405.17139, 101914-101958.
Scopus1
2023 Liu, Z., Sun, W., Hong, Y., Teney, D., & Gould, S. (2023). Bi-directional Training for Composed Image Retrieval via Text Prompt Learning.. CoRR, abs/2303.16604.
2023 Teney, D., Wang, J., & Abbasnejad, E. (2023). Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup.. CoRR, abs/2305.16817.
2023 Meadows, J., Valentino, M., Teney, D., & Freitas, A. (2023). A Symbolic Framework for Systematic Evaluation of Mathematical Reasoning with Transformers.. CoRR, abs/2305.12563.
2023 Liu, Z., Sun, W., Teney, D., & Gould, S. (2023). Candidate Set Re-ranking for Composed Image Retrieval with Dual Multi-modal Encoder.. CoRR, abs/2305.16304.
2023 Nicolicioiu, A. M., Nicolicioiu, A. L., Alexe, B., & Teney, D. (2023). Learning Diverse Features in Vision Transformers for Improved Generalization.. CoRR, abs/2308.16274.
2023 Lu, W., Yu, H., Wang, J., Teney, D., Wang, H., Chen, Y., . . . Ji, X. (2023). ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning.. CoRR, abs/2310.05143.
2023 Damirchi, H., Opazo, C. R., Abbasnejad, E., Teney, D., Shi, J. Q., Gould, S., & Hengel, A. V. D. (2023). Zero-shot Retrieval: Augmenting Pre-trained Models with Search Engines.. CoRR, abs/2311.17949.
2022 Dancette, C., Cadène, R., Teney, D., & Cord, M. (2022). Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answering.. CoRR, abs/2104.03149, 1554-1563.
DOI Scopus69 WoS66
2022 Shevchenko, V., Abbasnejad, E., Dick, A., Hengel, A. V. D., & Teney, D. (2022). EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual
Question Answering.
2022 Teney, D., Lin, Y., Oh, S. J., & Abbasnejad, E. (2022). ID and OOD Performance Are Sometimes Inversely Correlated on Real-world
Datasets.
2022 Shevchenko, V., Abbasnejad, E., Dick, A. R., Hengel, A. V. D., & Teney, D. (2022). EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering.. CoRR, abs/2206.14355.
2021 Shevchenko, V., Teney, D., Dick, A. R., & Hengel, A. V. D. (2021). Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledge.. CoRR, abs/2101.06013.
2020 Teney, D., Abbasnejad, E., & Hengel, A. V. D. (2020). Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision.. CoRR, abs/2004.09034.
2020 Shevchenko, V., Teney, D., Dick, A. R., & Hengel, A. V. D. (2020). Visual Question Answering with Prior Class Semantics.. CoRR, abs/2005.01239.
2020 Teney, D., Kafle, K., Shrestha, R., Abbasnejad, E., Kanan, C., & van den Hengel, A. (2020). On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020, 33, 11 pages.
2019 Teney, D., Wang, P., Cao, J., Liu, L., Shen, C., & Hengel, A. V. D. (2019). V-PROM: A Benchmark for Visual Reasoning Using Visual Progressive Matrices.. CoRR, abs/1907.12271, 12071-12078.
WoS15
2019 Teney, D., Abbasnejad, E., & Hengel, A. V. D. (2019). On Incorporating Semantic Prior Knowlegde in Deep Learning Through Embedding-Space Constraints.. CoRR, abs/1909.13471.
2017 Wu, Q., Teney, D., Wang, P., Shen, C., Dick, A., & van den Hengel, A. (2017). Visual question answering: a survey of methods and datasets. Computer Vision and Image Understanding, 163, 21-40.
DOI Scopus357 WoS270
2017 Teney, D., Wu, Q., & Van Den Hengel, A. (2017). Visual Question Answering: a tutorial. IEEE Signal Processing Magazine, 34(6), 63-75.
DOI Scopus36 WoS23
2017 Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S., & Zhang, L. (2017). Bottom-Up and Top-Down Attention for Image Captioning and VQA.. CoRR, abs/1707.07998.
2017 Wu, Q., Teney, D., Wang, P., Shen, C., Dick, A. R., & Hengel, A. V. D. (2017). Visual question answering: A survey of methods and datasets.. Comput. Vis. Image Underst., 163, 21-40.
DOI
2017 Teney, D., & Hengel, A. V. D. (2017). Visual Question Answering as a Meta Learning Task.. CoRR, abs/1711.08105.
2016 Teney, D., & Hengel, A. (2016). Zero-Shot Visual Question Answering.. CoRR, abs/1611.05546.
2014 Teney, D., & Piater, J. (2014). Multiview feature distributions for object detection and continuous pose estimation. Computer Vision and Image Understanding, 125, 265-282.
DOI Scopus21 WoS18

Year Citation
2025 Doan, B. G., Shamsi, A., Guo, X. Y., Mohammadi, A., Alinejad-Rokny, H., Sejdinovic, D., . . . Abbasnejad, E. (2025). Bayesian Low-Rank Learning (Bella): A Practical Approach to Bayesian Neural Networks. In T. Walsh, J. Shah, & Z. Kolter (Eds.), Proceedings of the AAAI Conference on Artificial Intelligence Vol. 39 (pp. 16298-16307). Philadelphia, Pennsylvania: Association for the Advancement of Artificial Intelligence (AAAI).
DOI Scopus9 WoS8
2024 Teney, D., Wang, J., & Abbasnejad, E. (2024). Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup. In Proceedings of Machine Learning Research Vol. 235 (pp. 47948-47964). Vienna: OpenReview.net.
Scopus3
2022 Teney, D., Abbasnejad, E., Lucey, S., & Hengel, A. V. D. (2022). Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022) Vol. 2022-June (pp. 16740-16751). New Orleans, Louisiana: IEEE.
DOI Scopus62 WoS39
2022 Liu, Z., Rodriguez-Opazo, C., Teney, D., & Gould, S. (2022). Image Retrieval on Real-life Images with Pre-trained Vision-and-Language Models. In Proceedings of the IEEE International Conference on Computer Vision (pp. 2105-2114). ELECTR NETWORK: IEEE.
DOI Scopus298 WoS187
2022 Parvaneh, A., Abbasnejad, E., Teney, D., Haffari, R., Van Den Hengel, A., & Shi, J. Q. (2022). Active Learning by Feature Mixing. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2022-June (pp. 12227-12236). New Orleans, LA, USA: IEEE.
DOI Scopus131 WoS111
2022 Teney, D., Peyrard, M., & Abbasnejad, E. (2022). Predicting Is Not Understanding: Recognizing and Addressing Underspecification in Machine Learning. In Conferenc proceedings Computer Vision ECCV Vol. 13683 (pp. 458-476). Tel Aviv, Israel: Springer Nature Switzerland.
DOI Scopus21 WoS11
2022 Hwang, I., Lee, S., Kwak, Y., Oh, S. J., Teney, D., Kim, J. H., & Zhang, B. T. (2022). SelecMix: Debiased Learning by Contradicting-pair Sampling. In Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022), as published in Advances in Neural Information Processing Systems Vol. 35. New Orleans & virtual online.
Scopus27
2022 Agrawal, A., Teney, D., & Nematzadeh, A. (2022). Vision-Language Pretraining: Current Trends and the Future. In L. Benotti, N. Okazaki, Y. Scherrer, & M. Zampieri (Eds.), Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 38-43). Association for Computational Linguistics.
DOI Scopus6
2021 Teney, D., Abbasnejad, E., & van den Hengel, A. (2021). Unshuffling Data for Improved Generalization in Visual Question Answering. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2021) (pp. 1397-1407). Los Alamitos, CA: IEEE.
DOI Scopus64 WoS48
2020 Teney, D., Kafle, K., Shrestha, R., Abbasnejad, E., Kanan, C., & Hengel, A. V. D. (2020). On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), Proceedings of the 34th Conference on Neural Information Processing Systems (NeruIPS 2020) Vol. abs/2005.09241 (pp. 1-11). San Francisco, CA, United States: Morgan Kaufmann.
Scopus104
2020 Liao, Z., Liu, L., Wu, Q., Teney, D., Shen, C., Van Den Hengel, A., & Verjans, J. (2020). Medical data inquiry using a question answering model. In Proceedings: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020) Vol. 2020-April (pp. 1490-1493). online: IEEE.
DOI Scopus10 WoS5
2020 Teney, D., Wang, P., Cao, J., Liu, L., Shen, C., & Van Den Hengel, A. (2020). V-PROM: A benchmark for visual reasoning using visual progressive matrices. In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) Vol. 34 (pp. 12071-12078). Palo Alto, CA: Association for the Advancement of Artificial Intelligence.
DOI Scopus25
2020 Abbasnejad, M., Teney, D., Parvaneh, A., Shi, Q., & Van Den Hengel, A. (2020). Counterfactual Vision and Language Learning.. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 10041-10051). online: IEEE.
DOI Scopus141 WoS108
2020 Teney, D., Abbasnejad, M., & Van Den Hengel, A. (2020). Learning what makes a difference from counterfactual examples and gradient supervision. In A. Vedaldi, H. Bischof, T. Brox, & J. -M. Frahm (Eds.), Proceedings of the 16th European Conference on Computer Vision Workshops (ECCV 2020), as published in Lecture Notes in Computer Science Vol. 12355 (pp. 580-599). Switzerland: Springer.
DOI Scopus72 WoS61
2020 Parvaneh, A., Abbasnejad, M., Teney, D., Shi, Q., & Van Den Hengel, A. (2020). Counterfactual Vision-and-Language Navigation: Unravelling the Unseen.. In H. Larochelle, M. Ranzato, R. Hadsell, M. -F. Balcan, & H. -T. Lin (Eds.), NeurIPS Vol. 2020-December (pp. 1-12). virtual online: NIPS.
Scopus37
2019 Teney, D., & Hengel, A. V. D. (2019). Actively seeking and learning from live data. In Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019) Vol. 2019-June (pp. 1940-1949). online: Computer Vision Foundation / IEEE.
DOI Scopus15 WoS14
2019 Teney, D., & Hengel, A. V. D. (2019). Actively Seeking and Learning From Live Data.. In CVPR (pp. 1940-1949). Computer Vision Foundation / IEEE.
2018 Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S., & Zhang, L. (2018). Bottom-up and top-down attention for image captioning and visual question answering. In Proceedings: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018) (pp. 6077-6086). Salt Lake City, UT: IEEE.
DOI Scopus4897 WoS3566
2018 Teney, D., Anderson, P., He, X., & Van Den Hengel, A. (2018). Tips and tricks for visual question answering: learnings from the 2017 challenge. In Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018) (pp. 4223-4232). Salt Lake City, USA: IEEE.
DOI Scopus298 WoS233
2018 Teney, D., & Van Den Hengel, A. (2018). Visual Question Answering as a meta learning task. In V. Ferrari, M. Hebert, C. Sminchisescu, & Y. Weiss (Eds.), Computer Vision - ECCV 2018: Proceedings, Part XV Vol. 11219 LNCS (pp. 229-245). Munich: Springer.
DOI Scopus8 WoS10
2018 Anderson, P., Wu, Q., Teney, D., Bruce, J., Johnson, M., Sünderhauf, N., . . . Hengel, A. V. D. (2018). Vision-and-language navigation: interpreting visually-grounded navigation instructions in real environments. In Proceedings: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018) Vol. abs/1711.07280 (pp. 3674-3683). Salt Lake City, UT: IEEE.
DOI Scopus1295 WoS1451
2018 Teney, D., Anderson, P., He, X., & Van Den Hengel, A. (2018). Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge.. In IEEE/CVF Conference on Computer Vision and Pattern Recognition Vol. abs/1708.02711 (pp. 4223-4232). Online: IEEE Computer Society.
DOI
2017 Teney, D., & Hebert, M. (2017). Learning to extract motion from videos in convolutional neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10115 LNCS (pp. 412-428). Taipei, Taiwan: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI Scopus30 WoS14
2017 Teney, D., Liu, L., & van den Hengel, A. (2017). Graph-structured representations for visual question answering. In Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) Vol. 2017-January (pp. 3233-3241). Online: IEEE.
DOI Scopus310 WoS292
2016 Teney, D., & Hebert, M. (2016). Learning to Extract Motion from Videos in Convolutional Neural Networks.. In S. -H. Lai, V. Lepetit, K. Nishino, & Y. Sato (Eds.), ACCV (5) Vol. 10115 (pp. 412-428). Springer.
2016 Teney, D., Liu, L., & Hengel, A. V. D. (2016). Graph-Structured Representations for Visual Question Answering.. In CoRR Vol. abs/1609.05600.
2015 Abbasnejad, I., & Teney, D. (2015). A hierarchical Bayesian network for face recognition using 2D and 3D facial data. In D. Erdogmus, M. Akcakaya, S. Kozat, & J. Larsen (Eds.), IEEE International Workshop on Machine Learning for Signal Processing, MLSP Vol. 2015-November (pp. 1-6). Boston, NY: IEEE.
DOI Scopus5 WoS1
2015 Teney, D., Brown, M., Kit, D., & Hall, P. (2015). Learning similarity metrics for dynamic scene segmentation. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 2084-2093). Boston, MA: IEEE.
DOI Scopus15 WoS8
2015 Saran, A., Teney, D., & Kitani, K. (2015). Hand parsing for fine-grained recognition of human grasps in monocular images. In IEEE International Conference on Intelligent Robots and Systems Vol. 2015-December (pp. 5052-5058). Hamburg, Germany: IEEE.
DOI Scopus11 WoS8
2014 Teney, D., & Brown, M. (2014). Segmentation of dynamic scenes with distributions of spatiotemporally oriented energies. In M. Valstar, A. French, & T. Pridmore (Eds.), BMVC 2014 - Proceedings of the British Machine Vision Conference 2014 (pp. 1-12). Nottingham: BMVA Press.
Scopus5
2014 Teney, D., & Brown, M. (2014). Segmentation of dynamic scenes with distributions of spatiotemporally oriented energies. In Bmvc 2014 Proceedings of the British Machine Vision Conference 2014 (pp. 37.1-37.12). British Machine Vision Association.
DOI Scopus6
2013 Teney, D., & Piater, J. (2013). Continuous pose estimation in 2D images at instance and category levels. In Proceedings of the 10th International Conference on Computer and Robot Vision (pp. 121-127). Regina, Canada: IEEE.
DOI Scopus8 WoS5
2013 Teney, D., & Piater, J. (2013). Modeling pose/appearance relations for improved object localization and pose estimation in 2D images. In J. Sanches, L. Mico, & J. Cardoso (Eds.), Pattern Recognition and Image Analysis Vol. 7887 LNCS (pp. 59-68). Porto: Springer.
DOI Scopus1 WoS1
2012 Teney, D., & Piater, J. (2012). Sampling-based multiview reconstruction without correspondences for 3D edges. In Proceedings of the 2nd Joint 3DIM/3DPVT Conference: 3D Imaging, Modeling, Processing, Visualization and Transmission (pp. 160-167). Zurich, Switerzland: IEEE.
DOI Scopus11 WoS5
2012 Teney, D., & Piater, J. (2012). Generalized exemplar-based full pose estimation from 2D images without correspondences. In Proceedings of the 2012 International Conference on Digital Image Computing Techniques and Applications (pp. 1-8). Fremantle, WA: IEEE.
DOI Scopus4
2011 Teney, D., & Piater, J. (2011). Probabilistic object models for pose estimation in 2D images. In R. Mester, & M. Felsberg (Eds.), Pattern Recognition Vol. 6835 LNCS (pp. 336-345). Frankfurt am Main, Germany: Springer.
DOI Scopus4

Year Citation
2025 Teney, D., Jiang, L., Gogianu, F., & Abbasnejad, E. (2025). Do We Always Need the Simplicity Bias? Looking for Optimal Inductive
Biases in the Wild.
2024 Teney, D., Nicolicioiu, A., Hartmann, V., & Abbasnejad, E. (2024). Neural Redshift: Random Networks are not Random Functions.
2024 Jiang, L., & Teney, D. (2024). OOD-Chameleon: Is Algorithm Selection for OOD Generalization Learnable?.
2020 Teney, D., Abbasnejad, E., & Hengel, A. V. D. (2020). Unshuffling Data for Improved Generalization..

Date Role Research Topic Program Degree Type Student Load Student Name
2018 - 2022 Co-Supervisor Developing a System for Free-Form Visual Question Answering Doctor of Philosophy Doctorate Full Time Miss Violetta Shevchenko

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