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2022 Conference Publication Learning to generate the unknowns as a remedy to the open-set domain shiftBaktashmotlagh, 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 |
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2022 Conference Publication Master of all: simultaneous generalization of urban-scene segmentation to all adverse weather conditionsReddy, 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 |
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2022 Conference Publication Modular construction planning using graph neural network heuristic searchHawkins, 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 |
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2021 Conference Publication Conditional Extreme Value Theory for Open Set Video Domain AdaptationChen, 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 |
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2021 Conference Publication Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant EmbeddingsMoskvyak, 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 |
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2021 Conference Publication Learning to diversify for single domain generalizationWang, 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 |
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2021 Conference Publication Neural-symbolic commonsense reasoner with relation predictorsMoghimifar, 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 |
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2021 Conference Publication Keypoint-aligned embeddings for image retrieval and re-identificationMoskvyak, 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 |
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2020 Conference Publication CosMo: Conditional Seq2Seq-based Mixture Model for Zero-Shot Commonsense Question AnsweringMoghimifar, 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 |
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Featured 2020 Journal Article Closing the gap of simulation to reality in electromagnetic imaging of brain strokes via deep neural networksAl-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 |
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2020 Conference Publication Learning causal Bayesian networks from textMoghimifar, 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. |
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2020 Conference Publication Few-shot single-view 3-D object reconstruction with compositional priorsMichalkiewicz, 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 |
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2020 Conference Publication Adversarial bipartite graph learning for video domain adaptationLuo, 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 |
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2020 Conference Publication Prototype-matching graph network for heterogeneous domain adaptationWang, 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 |
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2020 Conference Publication A Simple and Scalable Shape Representation for 3D ReconstructionMichalkiewicz, 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. |
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2020 Conference Publication Learning from the past: continual meta-learning with Bayesian Graph Neural NetworksLuo, 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 |
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2020 Journal Article Correlation-aware adversarial domain adaptation and generalizationRahman, 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 |
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2020 Conference Publication Learning Landmark Guided Embeddings for Animal Re-identificationMoskvyak, 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 |
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2020 Book Chapter On minimum discrepancy estimation for deep domain adaptationRahman, 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 |
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2020 Conference Publication Domain Adaptative Causality EncodeMoghimifar, Farhad, Haffari, Gholamreza and Baktashmotlagh, Mahsa (2020). Domain Adaptative Causality Encode. 18th Annual Workshop of the Australasian Language Technology Association, Online, 14–15 January 2021. Sydney, NSW Australia: Australasian Language Technology Association. |
