Mahsa Baktash

According to our database, Mahsa Baktash authored at least 73 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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

DiPEx: Dispersing Prompt Expansion for Class-Agnostic Object Detection.

Jia Syuen Lim, Zhuoxiao Chen, Mahsa Baktashmotlagh, Zhi Chen, Xin Yu, Zi Huang, Yadan Luo

CoRR, 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

Domain-Aware Knowledge Distillation for Continual Model Generalization.

Nikhil Reddy, Mahsa Baktashmotlagh, Chetan Arora

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

Leveraging LLMs for Unsupervised Dense Retriever Ranking.

Ekaterina Khramtsova, Shengyao Zhuang, Mahsa Baktashmotlagh, Guido Zuccon

Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 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

Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2024

Source-Free Domain-Invariant Performance Prediction.

Ekaterina Khramtsova, Mahsa Baktashmotlagh, Guido Zuccon, Xi Wang, Mathieu Salzmann

Proceedings of the Computer Vision - ECCV 2024, 2024

2023

Source-Free Progressive Graph Learning for Open-Set Domain Adaptation.

Yadan Luo, Zijian Wang, Zhuoxiao Chen, Zi Huang, Mahsa Baktashmotlagh

IEEE Trans. Pattern Anal. Mach. Intell., September, 2023

Interpretable Signed Link Prediction With Signed Infomax Hyperbolic Graph.

Yadan Luo, Zi Huang, Hongxu Chen, Yang Yang, Hongzhi Yin, Mahsa Baktashmotlagh

IEEE Trans. Knowl. Data Eng., April, 2023

DI-NIDS: Domain invariant network intrusion detection system.

Siamak Layeghy, Mahsa Baktashmotlagh, Marius Portmann

Knowl. Based Syst., 2023

FFM: Injecting Out-of-Domain Knowledge via Factorized Frequency Modification.

Zijian Wang, Yadan Luo, Zi Huang, Mahsa Baktashmotlagh

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

Center-aware Adversarial Augmentation for Single Domain Generalization.

Tianle Chen, Mahsa Baktashmotlagh, Zijian Wang, Mathieu Salzmann

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

Selecting which Dense Retriever to use for Zero-Shot Search.

Ekaterina Khramtsova, Shengyao Zhuang, Mahsa Baktashmotlagh, Xi Wang, Guido Zuccon

Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, 2023

Exploring Active 3D Object Detection from a Generalization Perspective.

Yadan Luo, Zhuoxiao Chen, Zijian Wang, Xin Yu, Zi Huang, Mahsa Baktashmotlagh

Proceedings of the Eleventh International Conference on Learning Representations, 2023

How Far Pre-trained Models Are from Neural Collapse on the Target Dataset Informs their Transferability.

Zijian Wang, Yadan Luo, Liang Zheng, Zi Huang, Mahsa Baktashmotlagh

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters.

Mateusz Michalkiewicz, Masoud Faraki, Xiang Yu, Manmohan Chandraker, Mahsa Baktashmotlagh

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Kecor: Kernel Coding Rate Maximization for Active 3D Object Detection.

Yadan Luo, Zhuoxiao Chen, Zhen Fang, Zheng Zhang, Mahsa Baktashmotlagh, Zi Huang

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling.

Zhuoxiao Chen, Yadan Luo, Zheng Wang, Mahsa Baktashmotlagh, Zi Huang

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Convolutional Persistence as a Remedy to Neural Model Analysis.

Ekaterina Khramtsova, Guido Zuccon, Xi Wang, Mahsa Baktashmotlagh

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Learning to Generate the Unknowns as a Remedy to the Open-Set Domain Shift.

Mahsa Baktashmotlagh, Tianle Chen, Mathieu Salzmann

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022

Rethinking Persistent Homology For Visual Recognition.

Ekaterina Khramtsova, Guido Zuccon, Xi Wang, Mahsa Baktashmotlagh

Proceedings of the Topological, 2022

Contrastive Class-aware Adaptation for Domain Generalization.

Tianle Chen, Mahsa Baktashmotlagh, Mathieu Salzmann

Proceedings of the 26th International Conference on Pattern Recognition, 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

Proceedings of the Computer Vision - ECCV 2022, 2022

Modular Construction Planning Using Graph Neural Network Heuristic Search.

Philip Hawkins, Frédéric Maire, Simon Denman, Mahsa Baktashmotlagh

Proceedings of the AI 2021: Advances in Artificial Intelligence, 2022

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, Amin M. Abbosh

IEEE Trans. Computational Imaging, 2021

Going Deeper into Semi-supervised Person Re-identification.

Olga Moskvyak, Frédéric Maire, Feras Dayoub, Mahsa Baktashmotlagh

CoRR, 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

Keypoint-Aligned Embeddings for Image Retrieval and Re-identification.

Olga Moskvyak, Frédéric Maire, Feras Dayoub, Mahsa Baktashmotlagh

Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2021

Conditional Extreme Value Theory for Open Set Video Domain Adaptation.

Zhuoxiao Chen, Yadan Luo, Mahsa Baktashmotlagh

Proceedings of the MMAsia '21: ACM Multimedia Asia, Gold Coast, Australia, December 1, 2021

Semi-supervised Keypoint Localization.

Olga Moskvyak, Frédéric Maire, Feras Dayoub, Mahsa Baktashmotlagh

Proceedings of the 9th International Conference on Learning Representations, 2021

Learning to Diversify for Single Domain Generalization.

Zijian Wang, Yadan Luo, Ruihong Qiu, Zi Huang, Mahsa Baktashmotlagh

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 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

Proceedings of the 2021 Digital Image Computing: Techniques and Applications, 2021

Neural-Symbolic Commonsense Reasoner with Relation Predictors.

Farhad Moghimifar, Lizhen Qu, Yue Zhuo, Gholamreza Haffari, Mahsa Baktashmotlagh

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021

2020

Correlation-aware adversarial domain adaptation and generalization.

Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh, Sridha Sridharan

Pattern Recognit., 2020

Domain Adaptative Causality Encoder.

Farhad Moghimifar, Gholamreza Haffari, Mahsa Baktashmotlagh

CoRR, 2020

Learning Causal Bayesian Networks from Text.

Farhad Moghimifar, Afshin Rahimi, Mahsa Baktashmotlagh, Xue Li

CoRR, 2020

Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph.

Yadan Luo, Zi Huang, Hongxu Chen, Yang Yang, Mahsa Baktashmotlagh

CoRR, 2020

Implicitly Defined Layers in Neural Networks.

Qianggong Zhang, Yanyang Gu, Mateusz Michalkiewicz, Mahsa Baktashmotlagh, Anders P. Eriksson

CoRR, 2020

Learning Landmark Guided Embeddings for Animal Re-identification.

Olga Moskvyak, Frédéric Maire, Feras Dayoub, Mahsa Baktashmotlagh

Proceedings of the IEEE Winter Applications of Computer Vision Workshops, 2020

Prototype-Matching Graph Network for Heterogeneous Domain Adaptation.

Zijian Wang, Yadan Luo, Zi Huang, Mahsa Baktashmotlagh

Proceedings of the MM '20: The 28th ACM International Conference on Multimedia, 2020

Adversarial Bipartite Graph Learning for Video Domain Adaptation.

Yadan Luo, Zi Huang, Zijian Wang, Zheng Zhang, Mahsa Baktashmotlagh

Proceedings of the MM '20: The 28th ACM International Conference on Multimedia, 2020

Progressive Graph Learning for Open-Set Domain Adaptation.

Yadan Luo, Zijian Wang, Zi Huang, Mahsa Baktashmotlagh

Proceedings of the 37th International Conference on Machine Learning, 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

Proceedings of the Computer Vision - ECCV 2020, 2020

CosMo: Conditional Seq2Seq-based Mixture Model for Zero-Shot Commonsense Question Answering.

Farhad Moghimifar, Lizhen Qu, Yue Zhuo, Mahsa Baktashmotlagh, Gholamreza Haffari

Proceedings of the 28th International Conference on Computational Linguistics, 2020

A Simple and Scalable Shape Representation for 3D Reconstruction.

Mateusz Michalkiewicz, Eugene Belilovsky, Mahsa Baktashmotlagh, Anders P. Eriksson

Proceedings of the 31st British Machine Vision Conference 2020, 2020

Learning from the Past: Continual Meta-Learning with Bayesian Graph Neural Networks.

Yadan Luo, Zi Huang, Zheng Zhang, Ziwei Wang, Mahsa Baktashmotlagh, Yang Yang

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

On Minimum Discrepancy Estimation for Deep Domain Adaptation.

Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh, Sridha Sridharan

Proceedings of the Domain Adaptation for Visual Understanding, 2020

2019

Visualizing Student Opinion Through Text Analysis.

Samuel Cunningham-Nelson, Mahsa Baktashmotlagh, Wageeh W. Boles

IEEE Trans. Educ., 2019

Learning from the Past: Continual Meta-Learning via Bayesian Graph Modeling.

Yadan Luo, Zi Huang, Zheng Zhang, Ziwei Wang, Mahsa Baktashmotlagh, Yang Yang

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

On Minimum Discrepancy Estimation for Deep Domain Adaptation.

Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh, Sridha Sridharan

CoRR, 2019

Multi-Component Image Translation for Deep Domain Generalization.

Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh, Sridha Sridharan

Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2019

Learning Factorized Representations for Open-Set Domain Adaptation.

Mahsa Baktashmotlagh, Masoud Faraki, Tom Drummond, Mathieu Salzmann

Proceedings of the 7th International Conference on Learning Representations, 2019

Implicit Surface Representations As Layers in Neural Networks.

Mateusz Michalkiewicz, Jhony Kaesemodel Pontes, Dominic Jack, Mahsa Baktashmotlagh, Anders P. Eriksson

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Object Graph Networks for Spatial Language Grounding.

Philip Hawkins, Frédéric Maire, Simon Denman, Mahsa Baktashmotlagh

Proceedings of the 2019 Digital Image Computing: Techniques and Applications, 2019

2017

Learning Domain Invariant Embeddings by Matching Distributions.

Mahsa Baktashmotlagh, Mehrtash Tafazzoli Harandi, Mathieu Salzmann

Proceedings of the Domain Adaptation in Computer Vision Applications., 2017

From Review to Rating: Exploring Dependency Measures for Text Classification.

Samuel Cunningham-Nelson, Mahsa Baktashmotlagh, Wageeh W. Boles

CoRR, 2017

Speaker verification with multi-run ICA based speech enhancement.

Ahmed Kamil Hasan Al-Ali, David Dean, Bouchra Senadji, Mahsa Baktashmotlagh, Vinod Chandran

Proceedings of the 11th International Conference on Signal Processing and Communication Systems, 2017

Deep discovery of facial motions using a shallow embedding layer.

Afsaneh Ghasemi, Mahsa Baktashmotlagh, Simon Denman, Sridha Sridharan, Dung Nguyen Tien, Clinton Fookes

Proceedings of the 2017 IEEE International Conference on Image Processing, 2017

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, Kotagiri Ramamohanarao

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Distribution-Matching Embedding for Visual Domain Adaptation.

Mahsa Baktashmotlagh, Mehrtash Tafazzoli Harandi, Mathieu Salzmann

J. Mach. Learn. Res., 2016

R1STM: One-class Support Tensor Machine with Randomised Kernel.

Sarah M. Erfani, Mahsa Baktashmotlagh, Sutharshan Rajasegarar, Vinh Nguyen, Christopher Leckie, James Bailey, Kotagiri Ramamohanarao

Proceedings of the 2016 SIAM International Conference on Data Mining, 2016

Robust Domain Generalisation by Enforcing Distribution Invariance.

Sarah M. Erfani, Mahsa Baktashmotlagh, Masud Moshtaghi, Vinh Nguyen, Christopher Leckie, James Bailey, Kotagiri Ramamohanarao

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

2015

Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs.

Mehrtash Tafazzoli Harandi, Mathieu Salzmann, Mahsa Baktashmotlagh

Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015

R1SVM: A Randomised Nonlinear Approach to Large-Scale Anomaly Detection.

Sarah M. Erfani, Mahsa Baktashmotlagh, Sutharshan Rajasegarar, Shanika Karunasekera, Christopher Leckie

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Learning Invariances for High-Dimensional Data Analysis

Mahsa Baktashmotlagh

PhD thesis, 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

Domain Adaptation on the Statistical Manifold.

Mahsa Baktashmotlagh, Mehrtash Tafazzoli Harandi, Brian C. Lovell, Mathieu Salzmann

Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014

2013

Non-Linear Stationary Subspace Analysis with Application to Video Classification.

Mahsa Baktashmotlagh, Mehrtash Tafazzoli Harandi, Abbas Bigdeli, Brian C. Lovell, Mathieu Salzmann

Proceedings of the 30th International Conference on Machine Learning, 2013

Unsupervised Domain Adaptation by Domain Invariant Projection.

Mahsa Baktashmotlagh, Mehrtash Tafazzoli Harandi, Brian C. Lovell, Mathieu Salzmann

Proceedings of the IEEE International Conference on Computer Vision, 2013

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

Proceedings of the 37th Annual IEEE Conference on Local Computer Networks, 2012

Directional Space-Time Oriented Gradients for 3D Visual Pattern Analysis.

Ehsan Norouznezhad, Mehrtash Tafazzoli Harandi, Abbas Bigdeli, Mahsa Baktash, Adam Postula, Brian C. Lovell

Proceedings of the Computer Vision - ECCV 2012, 2012

2011

Dynamic resource aware sensor networks: Integration of sensor cloud and ERPs.

Mahsa Baktashmotlagh, Abbas Bigdeli, Brian C. Lovell

Proceedings of the 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance, 2011