Dynamic resource aware sensor networks: Integration of sensor cloud and ERPs. - Mahsa Baktashmotlagh; Abbas Bigdeli; Brian C. Lovell (AVSS, 2011)

Directional Space-Time Oriented Gradients for 3D Visual Pattern Analysis. - Ehsan Norouznezhad; Mehrtash Tafazzoli Harandi; Abbas Bigdeli; Mahsa Baktash; Adam Postula; Brian C. Lovell (ECCV (3), 2012)

A wireless mesh sensor network for hazard and safety monitoring at the Port of Brisbane. - Amin Ahmadi; Abbas Bigdeli; Mahsa Baktashmotlagh; Brian C. Lovell (LCN, 2012)

Unsupervised Domain Adaptation by Domain Invariant Projection. - Mahsa Baktashmotlagh; Mehrtash Tafazzoli Harandi; Brian C. Lovell; Mathieu Salzmann (ICCV, 2013)

Non-Linear Stationary Subspace Analysis with Application to Video Classification. - Mahsa Baktashmotlagh; Mehrtash Tafazzoli Harandi; Abbas Bigdeli; Brian C. Lovell; Mathieu Salzmann (ICML (3), 2013)

Domain Adaptation on the Statistical Manifold. - Mahsa Baktashmotlagh; Mehrtash Tafazzoli Harandi; Brian C. Lovell; Mathieu Salzmann (CVPR, 2014)

Discriminative Non-Linear Stationary Subspace Analysis for Video Classification. - Mahsa Baktashmotlagh; Mehrtash Tafazzoli Harandi; Brian C. Lovell; Mathieu Salzmann (IEEE Trans. Pattern Anal. Mach. Intell., 2014)

Learning Invariances for High-Dimensional Data Analysis - Mahsa Baktashmotlagh (2014)

R1SVM: A Randomised Nonlinear Approach to Large-Scale Anomaly Detection. - Sarah M. Erfani; Mahsa Baktashmotlagh; Sutharshan Rajasegarar; Shanika Karunasekera; Christopher Leckie (AAAI, 2015)

Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs. - Mehrtash Tafazzoli Harandi; Mathieu Salzmann; Mahsa Baktashmotlagh (ICCV, 2015)

Robust Domain Generalisation by Enforcing Distribution Invariance. - Sarah M. Erfani; Mahsa Baktashmotlagh; Masud Moshtaghi; Vinh Nguyen; Christopher Leckie; James Bailey 001; Kotagiri Ramamohanarao (IJCAI, 2016)

R1STM: One-class Support Tensor Machine with Randomised Kernel. - Sarah M. Erfani; Mahsa Baktashmotlagh; Sutharshan Rajasegarar; Vinh Nguyen; Christopher Leckie; James Bailey 001; Kotagiri Ramamohanarao (SDM, 2016)

Distribution-Matching Embedding for Visual Domain Adaptation. - Mahsa Baktashmotlagh; Mehrtash Tafazzoli Harandi; Mathieu Salzmann (J. Mach. Learn. Res., 2016)

From Shared Subspaces to Shared Landmarks: A Robust Multi-Source Classification Approach. - Sarah M. Erfani; Mahsa Baktashmotlagh; Masud Moshtaghi; Vinh Nguyen; Christopher Leckie; James Bailey 001; Kotagiri Ramamohanarao (AAAI, 2017)

Deep discovery of facial motions using a shallow embedding layer. - Afsaneh Ghasemi; Mahsa Baktashmotlagh; Simon Denman; Sridha Sridharan; Dung Nguyen Tien; Clinton Fookes (ICIP, 2017)

Speaker verification with multi-run ICA based speech enhancement. - Ahmed Kamil Hasan Al-Ali; David Dean; Bouchra Senadji; Mahsa Baktashmotlagh; Vinod Chandran (ICSPCS, 2017)

From Review to Rating: Exploring Dependency Measures for Text Classification. - Samuel Cunningham-Nelson; Mahsa Baktashmotlagh; Wageeh W. Boles (CoRR, 2017)

Learning Domain Invariant Embeddings by Matching Distributions. - Mahsa Baktashmotlagh; Mehrtash Tafazzoli Harandi; Mathieu Salzmann (Domain Adaptation in Computer Vision Applications, 2017)

Object Graph Networks for Spatial Language Grounding. - Philip Hawkins; Frédéric Maire; Simon Denman; Mahsa Baktashmotlagh (DICTA, 2019)

Implicit Surface Representations As Layers in Neural Networks. - Mateusz Michalkiewicz; Jhony Kaesemodel Pontes; Dominic Jack; Mahsa Baktashmotlagh; Anders P. Eriksson (ICCV, 2019)

Learning Factorized Representations for Open-Set Domain Adaptation. - Mahsa Baktashmotlagh; Masoud Faraki; Tom Drummond; Mathieu Salzmann (ICLR (Poster), 2019)

Multi-Component Image Translation for Deep Domain Generalization. - Mohammad Mahfujur Rahman; Clinton Fookes; Mahsa Baktashmotlagh; Sridha Sridharan (WACV, 2019)

On Minimum Discrepancy Estimation for Deep Domain Adaptation. - Mohammad Mahfujur Rahman; Clinton Fookes; Mahsa Baktashmotlagh; Sridha Sridharan (CoRR, 2019)

Deep Level Sets: Implicit Surface Representations for 3D Shape Inference. - Mateusz Michalkiewicz; Jhony K. Pontes; Dominic Jack; Mahsa Baktashmotlagh; Anders P. Eriksson (CoRR, 2019)

Learning from the Past: Continual Meta-Learning via Bayesian Graph Modeling. - Yadan Luo; Zi Huang; Zheng Zhang 006; Ziwei Wang 003; Mahsa Baktashmotlagh; Yang Yang 002 (CoRR, 2019)

Visualizing Student Opinion Through Text Analysis. - Samuel Cunningham-Nelson; Mahsa Baktashmotlagh; Wageeh W. Boles (IEEE Trans. Educ., 2019)

On Minimum Discrepancy Estimation for Deep Domain Adaptation. - Mohammad Mahfujur Rahman; Clinton Fookes; Mahsa Baktashmotlagh; Sridha Sridharan (Domain Adaptation for Visual Understanding, 2020)

Learning from the Past: Continual Meta-Learning with Bayesian Graph Neural Networks. - Yadan Luo; Zi Huang; Zheng Zhang 006; Ziwei Wang 003; Mahsa Baktashmotlagh; Yang Yang 002 (AAAI, 2020)

Learning Causal Bayesian Networks from Text. - Farhad Moghimifar; Afshin Rahimi 001; Mahsa Baktashmotlagh; Xue Li (ALTA, 2020)

Domain Adaptative Causality Encoder. - Farhad Moghimifar; Gholamreza Haffari; Mahsa Baktashmotlagh (ALTA, 2020)

A Simple and Scalable Shape Representation for 3D Reconstruction. - Mateusz Michalkiewicz; Eugene Belilovsky; Mahsa Baktashmotlagh; Anders P. Eriksson (BMVC, 2020)

CosMo: Conditional Seq2Seq-based Mixture Model for Zero-Shot Commonsense Question Answering. - Farhad Moghimifar; Lizhen Qu; Yue Zhuo 001; Mahsa Baktashmotlagh; Gholamreza Haffari (COLING, 2020)

Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors. - Mateusz Michalkiewicz; Sarah Parisot; Stavros Tsogkas; Mahsa Baktashmotlagh; Anders P. Eriksson; Eugene Belilovsky (ECCV (25), 2020)

Progressive Graph Learning for Open-Set Domain Adaptation. - Yadan Luo; Zijian Wang 009; Zi Huang; Mahsa Baktashmotlagh (ICML, 2020)

Adversarial Bipartite Graph Learning for Video Domain Adaptation. - Yadan Luo; Zi Huang; Zijian Wang 009; Zheng Zhang 006; Mahsa Baktashmotlagh (ACM Multimedia, 2020)

Prototype-Matching Graph Network for Heterogeneous Domain Adaptation. - Zijian Wang 009; Yadan Luo; Zi Huang; Mahsa Baktashmotlagh (ACM Multimedia, 2020)

Learning Landmark Guided Embeddings for Animal Re-identification. - Olga Moskvyak; Frédéric Maire; Feras Dayoub; Mahsa Baktashmotlagh (WACV Workshops, 2020)

Implicitly Defined Layers in Neural Networks. - Qianggong Zhang; Yanyang Gu; Mateusz Michalkiewicz; Mahsa Baktashmotlagh; Anders P. Eriksson (CoRR, 2020)

Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph. - Yadan Luo; Zi Huang; Hongxu Chen 002; Yang Yang 002; Mahsa Baktashmotlagh (CoRR, 2020)

Correlation-aware adversarial domain adaptation and generalization. - Mohammad Mahfujur Rahman; Clinton Fookes; Mahsa Baktashmotlagh; Sridha Sridharan (Pattern Recognit., 2020)

Neural-Symbolic Commonsense Reasoner with Relation Predictors. - Farhad Moghimifar; Lizhen Qu; Yue Zhuo 001; Gholamreza Haffari; Mahsa Baktashmotlagh (ACL/IJCNLP (2), 2021)

Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant Embeddings. - Olga Moskvyak; Frédéric Maire; Feras Dayoub; Asia O. Armstrong; Mahsa Baktashmotlagh (DICTA, 2021)

Learning to Diversify for Single Domain Generalization. - Zijian Wang 009; Yadan Luo; Ruihong Qiu; Zi Huang; Mahsa Baktashmotlagh (ICCV, 2021)

Semi-supervised Keypoint Localization. - Olga Moskvyak; Frédéric Maire; Feras Dayoub; Mahsa Baktashmotlagh (ICLR, 2021)

Conditional Extreme Value Theory for Open Set Video Domain Adaptation. - Zhuoxiao Chen; Yadan Luo; Mahsa Baktashmotlagh (MMAsia, 2021)

Keypoint-Aligned Embeddings for Image Retrieval and Re-identification. - Olga Moskvyak; Frédéric Maire; Feras Dayoub; Mahsa Baktashmotlagh (WACV, 2021)

Learning Compositional Shape Priors for Few-Shot 3D Reconstruction. - Mateusz Michalkiewicz; Stavros Tsogkas; Sarah Parisot; Mahsa Baktashmotlagh; Anders P. Eriksson; Eugene Belilovsky (CoRR, 2021)

Going Deeper into Semi-supervised Person Re-identification. - Olga Moskvyak; Frédéric Maire; Feras Dayoub; Mahsa Baktashmotlagh (CoRR, 2021)

Closing the Gap of Simulation to Reality in Electromagnetic Imaging of Brain Strokes via Deep Neural Networks. - Ahmed Al-Saffar; Alina Bialkowski; Mahsa Baktashmotlagh; Adnan Trakic; Lei Guo 007; Amin M. Abbosh (IEEE Trans. Computational Imaging, 2021)

Modular Construction Planning Using Graph Neural Network Heuristic Search. - Philip Hawkins; Frédéric Maire; Simon Denman; Mahsa Baktashmotlagh (AI, 2022)

Master of All: Simultaneous Generalization of Urban-Scene Segmentation to All Adverse Weather Conditions. - Nikhil Reddy; Abhinav Singhal; Abhishek Kumar; Mahsa Baktashmotlagh; Chetan Arora 001 (ECCV (39), 2022)

Contrastive Class-aware Adaptation for Domain Generalization. - Tianle Chen; Mahsa Baktashmotlagh; Mathieu Salzmann (ICPR, 2022)

Rethinking Persistent Homology For Visual Recognition. - Ekaterina Khramtsova; Guido Zuccon; Xi Wang 021; Mahsa Baktashmotlagh (TAG-ML, 2022)

Learning to Generate the Unknowns as a Remedy to the Open-Set Domain Shift. - Mahsa Baktashmotlagh; Tianle Chen; Mathieu Salzmann (WACV, 2022)

Convolutional Persistence as a Remedy to Neural Model Analysis. - Ekaterina Khramtsova; Guido Zuccon; Xi Wang 021; Mahsa Baktashmotlagh (AISTATS, 2023)

Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling. - Zhuoxiao Chen; Yadan Luo; Zheng Wang 044; Mahsa Baktashmotlagh; Zi Huang (ICCV, 2023)

Kecor: Kernel Coding Rate Maximization for Active 3D Object Detection. - Yadan Luo; Zhuoxiao Chen; Zhen Fang 001; Zheng Zhang 006; Mahsa Baktashmotlagh; Zi Huang (ICCV, 2023)

Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters. - Mateusz Michalkiewicz; Masoud Faraki; Xiang Yu 002; Manmohan Chandraker; Mahsa Baktashmotlagh (ICCV, 2023)

How Far Pre-trained Models Are from Neural Collapse on the Target Dataset Informs their Transferability. - Zijian Wang 009; Yadan Luo; Liang Zheng 001; Zi Huang; Mahsa Baktashmotlagh (ICCV, 2023)

Exploring Active 3D Object Detection from a Generalization Perspective. - Yadan Luo; Zhuoxiao Chen; Zijian Wang 009; Xin Yu 002; Zi Huang; Mahsa Baktashmotlagh (ICLR, 2023)

Selecting which Dense Retriever to use for Zero-Shot Search. - Ekaterina Khramtsova; Shengyao Zhuang; Mahsa Baktashmotlagh; Xi Wang 021; Guido Zuccon (SIGIR-AP, 2023)

Center-aware Adversarial Augmentation for Single Domain Generalization. - Tianle Chen; Mahsa Baktashmotlagh; Zijian Wang 009; Mathieu Salzmann (WACV, 2023)

FFM: Injecting Out-of-Domain Knowledge via Factorized Frequency Modification. - Zijian Wang 009; Yadan Luo; Zi Huang; Mahsa Baktashmotlagh (WACV, 2023)

DI-NIDS: Domain invariant network intrusion detection system. - Siamak Layeghy; Mahsa Baktashmotlagh; Marius Portmann (Knowl. Based Syst., 2023)

Interpretable Signed Link Prediction With Signed Infomax Hyperbolic Graph. - Yadan Luo; Zi Huang; Hongxu Chen 002; Yang Yang 002; Hongzhi Yin; Mahsa Baktashmotlagh (IEEE Trans. Knowl. Data Eng., 2023)

Source-Free Progressive Graph Learning for Open-Set Domain Adaptation. - Yadan Luo; Zijian Wang 009; Zhuoxiao Chen; Zi Huang; Mahsa Baktashmotlagh (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Source-Free Domain-Invariant Performance Prediction. - Ekaterina Khramtsova; Mahsa Baktashmotlagh; Guido Zuccon; Xi Wang 021; Mathieu Salzmann (ECCV (80), 2024)

DiPEx: Dispersing Prompt Expansion for Class-Agnostic Object Detection. - Jia Syuen Lim; Zhuoxiao Chen; Zhi Chen 010; Mahsa Baktashmotlagh; Xin Yu 002; Zi Huang; Yadan Luo (NeurIPS, 2024)

Color-Oriented Redundancy Reduction in Dataset Distillation. - Bowen Yuan; Zijian Wang 009; Mahsa Baktashmotlagh; Yadan Luo; Zi Huang (NeurIPS, 2024)

Embark on DenseQuest: A System for Selecting the Best Dense Retriever for a Custom Collection. - Ekaterina Khramtsova; Teerapong Leelanupab; Shengyao Zhuang; Mahsa Baktashmotlagh; Guido Zuccon (SIGIR, 2024)

Leveraging LLMs for Unsupervised Dense Retriever Ranking. - Ekaterina Khramtsova; Shengyao Zhuang; Mahsa Baktashmotlagh; Guido Zuccon (SIGIR, 2024)

Domain-Aware Knowledge Distillation for Continual Model Generalization. - Nikhil Reddy; Mahsa Baktashmotlagh; Chetan Arora 001 (WACV, 2024)

MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detection. - Zhuoxiao Chen; Junjie Meng; Mahsa Baktashmotlagh; Zi Huang; Yadan Luo (CoRR, 2024)

Shape-Space Deformer: Unified Visuo-Tactile Representations for Robotic Manipulation of Deformable Objects. - Sean M. V. Collins; Brendan Tidd; Mahsa Baktashmotlagh; Peyman Moghadam (CoRR, 2024)

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models. - Mateusz Michalkiewicz; Sheena Bai; Mahsa Baktashmotlagh; Varun Jampani; Guha Balakrishnan (CoRR, 2024)

MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detection. - Zhuoxiao Chen; Junjie Meng; Mahsa Baktashmotlagh; Yonggang Zhang 003; Zi Huang; Yadan Luo (ICLR, 2025)

Feature Space Perturbation: A Panacea to Enhanced Transferability Estimation. - Prafful Kumar Khoba; Zijian Wang 009; Chetan Arora 001; Mahsa Baktashmotlagh (WACV, 2025)

Benchmarking Transferability: A Framework for Fair and Robust Evaluation. - Alireza Kazemi; Helia Rezvani; Mahsa Baktashmotlagh (CoRR, 2025)

SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images. - Kaiyu Guo; Tan Pan; Chen Jiang; Zijian Wang 009; Brian C. Lovell; Limei Han; Yuan Cheng; Mahsa Baktashmotlagh (CoRR, 2025)

WisWheat: A Three-Tiered Vision-Language Dataset for Wheat Management. - Bowen Yuan; Selena Song; Javier Fernandez; Yadan Luo; Mahsa Baktashmotlagh; Zijian Wang 009 (CoRR, 2025)

GIQ: Benchmarking 3D Geometric Reasoning of Vision Foundation Models with Simulated and Real Polyhedra. - Mateusz Michalkiewicz; Anekha Sokhal; Tadeusz Michalkiewicz; Piotr Pawlikowski; Mahsa Baktashmotlagh; Varun Jampani; Guha Balakrishnan (CoRR, 2025)

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration. - Kaiyu Guo; Zijian Wang 009; Tan Pan; Brian C. Lovell; Mahsa Baktashmotlagh (CoRR, 2025)