Damien Teney

School of Computer and Mathematical Sciences

Faculty of Sciences, Engineering and Technology

Eligible to supervise Masters and PhD - email supervisor to discuss availability.

My Research
Publications
Supervision
Contact

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.

Journals

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.
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.
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.
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.
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., Hengel, A. V. D., & Teney, D. (2022). EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering.
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 Scopus45 WoS13
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., 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.
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.
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. WoS4
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	Teney, D., & Hengel, A. V. D. (2017). Visual Question Answering as a Meta Learning Task.. CoRR, abs/1711.08105.
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., Wu, Q., & Van Den Hengel, A. (2017). Visual Question Answering: a tutorial. IEEE Signal Processing Magazine, 34(6), 63-75. DOI Scopus33 WoS17
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 Scopus284 WoS156
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 WoS16

Conference Papers

Year	Citation
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.
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 Scopus45 WoS5
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 Scopus125 WoS5
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 Scopus88 WoS13
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 Scopus9 WoS2
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. Scopus12
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 Scopus4
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) Vol. abs/2002.11894 (pp. 1397-1407). Los Alamitos, CA: IEEE. DOI Scopus45 WoS5
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. Scopus85
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 Scopus8 WoS3
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 Scopus19
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 Scopus115
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 Scopus60
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. Scopus27
2019	Teney, D., & Hengel, A. V. D. (2019). Actively Seeking and Learning From Live Data.. In CVPR (pp. 1940-1949). Computer Vision Foundation / IEEE.
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 Scopus12 WoS4
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
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 Scopus4061 WoS1101
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 Scopus274 WoS158
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 Scopus7 WoS4
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 Scopus916 WoS1140
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 Scopus283 WoS180
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 WoS12
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
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 Scopus12 WoS7
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 WoS7
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 Scopus5
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). 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
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
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

Preprint

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	Jiang, L., & Teney, D. (2024). OOD-Chameleon: Is Algorithm Selection for OOD Generalization Learnable?.
2024	Teney, D., Nicolicioiu, A., Hartmann, V., & Abbasnejad, E. (2024). Neural Redshift: Random Networks are not Random Functions.

Past Higher Degree by Research Supervision (University of Adelaide)

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

Email: damien.teney@adelaide.edu.au

Damien Teney

Journals

Conference Papers

Preprint

Past Higher Degree by Research Supervision (University of Adelaide)

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