Overview
Background
Mahsa Baktashmotlagh is currently an Associate Professor and an ARC Future Fellow at UQ, developing machine learning techniques applied in: Visual data analysis, Biomedical data (Antibacterial activity prediction), and Cyber Security.
Availability
- Associate Professor Mahsa Baktashmotlagh is:
- Available for supervision
Qualifications
- Doctor of Philosophy, The University of Queensland
Works
Search Professor Mahsa Baktashmotlagh’s works on UQ eSpace
2022
Conference Publication
Rethinking persistent homology for visual recognition
Khramtsova, Ekaterina, Zuccon, Guido, Wang, Xi and Baktashmotlagh, Mahsa (2022). Rethinking persistent homology for visual recognition. Topological, Algebraic and Geometric Learning Workshops, Online, 25-22 July 2022. Brookline, MA United States: ML Research Press.
2022
Conference Publication
Learning to generate the unknowns as a remedy to the open-set domain shift
Baktashmotlagh, Mahsa, Chen, Tianle and Salzmann, Mathieu (2022). Learning to generate the unknowns as a remedy to the open-set domain shift. 22nd IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, United States, 3-8 January 2022. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/WACV51458.2022.00379
2022
Conference Publication
Master of all: simultaneous generalization of urban-scene segmentation to all adverse weather conditions
Reddy, Nikhil, Singhal, Abhinav, Kumar, Abhishek, Baktashmotlagh, Mahsa and Arora, Chetan (2022). Master of all: simultaneous generalization of urban-scene segmentation to all adverse weather conditions. Computer Vision – ECCV 2022, Tel Aviv, Israel, 23-27 October 2022. Heidelberg, Germany: Springer. doi: 10.1007/978-3-031-19842-7_4
2022
Conference Publication
Modular construction planning using graph neural network heuristic search
Hawkins, Philip, Maire, Frederic, Denman, Simon and Baktashmotlagh, Mahsa (2022). Modular construction planning using graph neural network heuristic search. 34th Australasian Joint Conference on Artificial Intelligence (AI), Electr Network, 2-4 February 2022. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-97546-3_19
2021
Conference Publication
Conditional Extreme Value Theory for Open Set Video Domain Adaptation
Chen, Zhuoxiao, Luo, Yadan and Baktashmotlagh, Mahsa (2021). Conditional Extreme Value Theory for Open Set Video Domain Adaptation. MMAsia '21: ACM Multimedia Asia, Gold Coast, QLD Australia, 1 - 3 December 2021. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3469877.3490600
2021
Conference Publication
Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant Embeddings
Moskvyak, Olga, Maire, Frederic, Dayoub, Feras, Armstrong, Asia O. and Baktashmotlagh, Mahsa (2021). Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant Embeddings. 2021 Digital Image Computing: Techniques and Applications (DICTA), Gold Coast, QLD Australia, 29 November 2021 - 1 December 2021. Piscataway, NJ United States: IEEE. doi: 10.1109/dicta52665.2021.9647359
2021
Conference Publication
Learning to diversify for single domain generalization
Wang, Zijian, Luo, Yadan, Qiu, Ruihong, Huang, Zi and Baktashmotlagh, Mahsa (2021). Learning to diversify for single domain generalization. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC Canada, 10-17 October 2021. Piscataway, NJ USA: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICCV48922.2021.00087
2021
Conference Publication
Keypoint-aligned embeddings for image retrieval and re-identification
Moskvyak, Olga, Maire, Frederic, Dayoub, Feras and Baktashmotlagh, Mahsa (2021). Keypoint-aligned embeddings for image retrieval and re-identification. IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, United States, 3-8 January 2021. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/WACV48630.2021.00072
2021
Conference Publication
Neural-symbolic commonsense reasoner with relation predictors
Moghimifar, Farhad, Qu, Lizhen, Zhuo, Yue, Haffari, Gholamreza and Baktashmotlagh, Mahsa (2021). Neural-symbolic commonsense reasoner with relation predictors. 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Online, 1-6 August 2021. Stroudsburg, PA, United States: Association for Computational Linguistics (ACL). doi: 10.18653/v1/2021.acl-short.100
2020
Conference Publication
CosMo: Conditional Seq2Seq-based Mixture Model for Zero-Shot Commonsense Question Answering
Moghimifar, Farhad, Qu, Lizhen, Zhuo, Yue, Baktashmotlagh, Mahsa and Haffari, Gholamreza (2020). CosMo: Conditional Seq2Seq-based Mixture Model for Zero-Shot Commonsense Question Answering. 28th International Conference on Computational Linguistics, Barcelona, Spain, 8-13 December 2020. Stroudsburg, PA United States: International Committee on Computational Linguistics. doi: 10.18653/v1/2020.coling-main.467
2020
Journal Article
Closing the gap of simulation to reality in electromagnetic imaging of brain strokes via deep neural networks
Al-Saffar, Ahmed, Bialkowski, Alina, Baktashmotlagh, Mahsa, Trakic, Adnan, Guo, Lei and Abbosh, Amin (2020). Closing the gap of simulation to reality in electromagnetic imaging of brain strokes via deep neural networks. IEEE Transactions on Computational Imaging, 7 9274540, 13-21. doi: 10.1109/tci.2020.3041092
2020
Conference Publication
Learning causal Bayesian networks from text
Moghimifar, Farhad, Rahimi, Afshin, Baktashmotlagh, Mahsa and Li, Xue (2020). Learning causal Bayesian networks from text. The 18th Annual Workshop of the Australasian Language Technology Association, Virtual, 14-15 January 2021. Australasian Language Technology Association.
2020
Conference Publication
Few-shot single-view 3-D object reconstruction with compositional priors
Michalkiewicz, Mateusz, Parisot, Sarah, Tsogkas, Stavros, Baktashmotlagh, Mahsa, Eriksson, Anders and Belilovsky, Eugene (2020). Few-shot single-view 3-D object reconstruction with compositional priors. Computer Vision – ECCV 2020, Glasgow, United Kingdom, 23-28 August 2020. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-58595-2_37
2020
Conference Publication
Prototype-matching graph network for heterogeneous domain adaptation
Wang, Zijian, Luo, Yadan, Huang, Zi and Baktashmotlagh, Mahsa (2020). Prototype-matching graph network for heterogeneous domain adaptation. MM '20: 28th ACM International Conference on Multimedia, Online, October 2020. New York, NY, United States: ACM. doi: 10.1145/3394171.3413662
2020
Conference Publication
Adversarial bipartite graph learning for video domain adaptation
Luo, Yadan, Huang, Zi, Wang, Zijian, Zhang, Zheng and Baktashmotlagh, Mahsa (2020). Adversarial bipartite graph learning for video domain adaptation. ACM International Conference on Multimedia, Seattle, WA, United States, 12-16 October 2020. New York, United States: Association for Computing Machinery. doi: 10.1145/3394171.3413897
2020
Conference Publication
A Simple and Scalable Shape Representation for 3D Reconstruction
Michalkiewicz, Mateusz, Belilovsky, Eugene, Baktashmotlagh, Mahsa and Eriksson, Anders (2020). A Simple and Scalable Shape Representation for 3D Reconstruction. 31st British Machine Vision Conference, BMVC 2020, Online, 7 - 10 September 2020. Durham, United Kingdom: British Machine Vision Association.
2020
Conference Publication
Learning from the past: continual meta-learning with Bayesian Graph Neural Networks
Luo, Yadan, Huang, Zi, Zhang, Zheng, Wang, Ziwei, Baktashmotlagh, Mahsa and Yang, Yang (2020). Learning from the past: continual meta-learning with Bayesian Graph Neural Networks. The Thirty-Fourth AAAI Conference on Artificial Intelligence/ The Thirty-Second Conference on Innovative Applications of Artificial Intelligence/ The Tenth Symposium on Educational Advances in Artificial Intelligence, New York, United States, 7-12 February 2020. Palo Alto, CA, United States: Association for the Advancement of Artificial Intelligence (AAAI). doi: 10.1609/aaai.v34i04.5942
2020
Journal Article
Correlation-aware adversarial domain adaptation and generalization
Rahman, Mohammad Mahfujur, Fookes, Clinton, Baktashmotlagh, Mahsa and Sridharan, Sridha (2020). Correlation-aware adversarial domain adaptation and generalization. Pattern Recognition, 100 107124. doi: 10.1016/j.patcog.2019.107124
2020
Conference Publication
Learning Landmark Guided Embeddings for Animal Re-identification
Moskvyak, Olga, Maire, Frederic, Dayoub, Feras and Baktashmotlagh, Mahsa (2020). Learning Landmark Guided Embeddings for Animal Re-identification. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Snowmass, CO United States, 1-5 March 2020. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/WACVW50321.2020.9096932
2020
Book Chapter
On minimum discrepancy estimation for deep domain adaptation
Rahman, Mohammad Mahfujur, Fookes, Clinton, Baktashmotlagh, Mahsa and Sridharan, Sridha (2020). On minimum discrepancy estimation for deep domain adaptation. Domain adaptation for visual understanding. (pp. 81-94) edited by Richa Singh, Mayank Vatsa, Vishal M. Patel and Nalini Ratha. Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-030-30671-7_6
Supervision
Availability
- Associate Professor Mahsa Baktashmotlagh is:
- Available for supervision
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Supervision history
Current supervision
-
Doctor Philosophy
Enhancing Safety and Reliability of machine learning models using lifelong multi-modal learning
Principal Advisor
Other advisors: Professor Helen Huang, Dr Yadan Luo
-
Doctor Philosophy
Generalizing Implicit Representations for Robotics Manipulation of Articulated Objects
Principal Advisor
Other advisors: Dr Peyman Moghadam
-
Doctor Philosophy
Parametric Deep Neural Networks for Computer Vision Problems
Principal Advisor
-
Doctor Philosophy
Revisiting Assumptions and Evaluation Metrics in Domain Generalization
Principal Advisor
-
Doctor Philosophy
Enhancing Robustness and Generalizability in Computational Models
Principal Advisor
Other advisors: Dr Xin Yu
-
Doctor Philosophy
Semantic Segmentation for Crop Health and Damage Assessment
Principal Advisor
-
Doctor Philosophy
Enhancing Plant Phenotyping Accuracy through Analysing Video Data
Principal Advisor
Other advisors: Professor Scott Chapman, Dr Yadan Luo
-
Doctor Philosophy
Unsupervised Domain Adaptation on 3D Object Detection and Segmentation
Associate Advisor
Other advisors: Professor Helen Huang, Dr Yadan Luo
-
Doctor Philosophy
Out-of-Distribution Generalisation and Detection in Feature Embedding Space
Associate Advisor
Other advisors: Professor Brian Lovell
-
Doctor Philosophy
Automatic Retinal Health Monitoring through Multi-modal Medical Imaging
Associate Advisor
Other advisors: Dr Xin Yu
-
Completed supervision
-
2024
Doctor Philosophy
Exploring Facets of Model Generalizability on Out-of-Distribution Data
Principal Advisor
Other advisors: Professor Guido Zuccon
-
2023
Doctor Philosophy
Monocular 3D Reconstruction: Shape Representation, Scalability and Generalization.
Principal Advisor
Other advisors: Dr Yadan Luo
-
2022
Doctor Philosophy
On Encoding Causality for Natural Language Understanding
Principal Advisor
Other advisors: Professor Xue Li
-
2024
Doctor Philosophy
Towards Analysis of Contextual Melanoma Indicators and Identification of Total-Body Ugly Duckling Lesions with Deep Neural Networks
Associate Advisor
Other advisors: Dr Shakes Chandra
-
2023
Doctor Philosophy
On improving vision model transferability to address domain shift in an open world
Associate Advisor
Other advisors: Professor Helen Huang
-
2021
Doctor Philosophy
Visual Learning from Imperfect Data via Inductive Bias Modelling
Associate Advisor
Other advisors: Professor Helen Huang
Media
Enquiries
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