Search Results for author: Mahsa Baktashmotlagh

Found 30 papers, 10 papers with code

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

1 code implementation13 Feb 2022 Yadan Luo, Zijian Wang, Zhuoxiao Chen, Zi Huang, Mahsa Baktashmotlagh

However, most existing OSDA approaches are limited due to three main reasons, including: (1) the lack of essential theoretical analysis of generalization bound, (2) the reliance on the coexistence of source and target data during adaptation, and (3) failing to accurately estimate the uncertainty of model predictions.

Action Recognition Domain Adaptation +2

Conditional Extreme Value Theory for Open Set Video Domain Adaptation

1 code implementation1 Sep 2021 Zhuoxiao Chen, Yadan Luo, Mahsa Baktashmotlagh

The majority of video domain adaptation algorithms are proposed for closed-set scenarios in which all the classes are shared among the domains.

Action Recognition Domain Adaptation

Learning to Diversify for Single Domain Generalization

1 code implementation ICCV 2021 Zijian Wang, Yadan Luo, Ruihong Qiu, Zi Huang, Mahsa Baktashmotlagh

Domain generalization (DG) aims to generalize a model trained on multiple source (i. e., training) domains to a distributionally different target (i. e., test) domain.

Domain Generalization

Going Deeper into Semi-supervised Person Re-identification

no code implementations24 Jul 2021 Olga Moskvyak, Frederic Maire, Feras Dayoub, Mahsa Baktashmotlagh

To reduce the need for labeled data, we focus on a semi-supervised approach that requires only a subset of the training data to be labeled.

Semi-Supervised Person Re-Identification

Learning Compositional Shape Priors for Few-Shot 3D Reconstruction

no code implementations11 Jun 2021 Mateusz Michalkiewicz, Stavros Tsogkas, Sarah Parisot, Mahsa Baktashmotlagh, Anders Eriksson, Eugene Belilovsky

The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of the output space.

3D Reconstruction Few-Shot Learning +1

Semi-supervised Keypoint Localization

no code implementations ICLR 2021 Olga Moskvyak, Frederic Maire, Feras Dayoub, Mahsa Baktashmotlagh

Keypoint representations are learnt with a semantic keypoint consistency constraint that forces the keypoint detection network to learn similar features for the same keypoint across the dataset.

Keypoint Detection

Learning to Generate the Unknowns for Open-set Domain Adaptation

no code implementations1 Jan 2021 Mahsa Baktashmotlagh, Tianle Chen, Mathieu Salzmann

In this setting, existing techniques focus on the challenging task of isolating the unknown target samples, so as to avoid the negative transfer resulting from aligning the source feature distributions with the broader target one that encompasses the additional unknown classes.

Domain Adaptation

Domain Adaptative Causality Encoder

1 code implementation ALTA 2020 Farhad Moghimifar, Gholamreza Haffari, Mahsa Baktashmotlagh

Our experiments on four different benchmark causality datasets demonstrate the superiority of our approach over the existing baselines, by up to 7% improvement, on the tasks of identification and localisation of the causal relations from the text.

Learning Causal Bayesian Networks from Text

no code implementations ALTA 2020 Farhad Moghimifar, Afshin Rahimi, Mahsa Baktashmotlagh, Xue Li

Causal relationships form the basis for reasoning and decision-making in Artificial Intelligence systems.

Decision Making

Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph

1 code implementation25 Nov 2020 Yadan Luo, Zi Huang, Hongxu Chen, Yang Yang, Mahsa Baktashmotlagh

Most of the prior efforts are devoted to learning node embeddings with graph neural networks (GNNs), which preserve the signed network topology by message-passing along edges to facilitate the downstream link prediction task.

Link Prediction

COSMO: Conditional SEQ2SEQ-based Mixture Model for Zero-Shot Commonsense Question Answering

1 code implementation COLING 2020 Farhad Moghimifar, Lizhen Qu, Yue Zhuo, Mahsa Baktashmotlagh, Gholamreza Haffari

However, current approaches in this realm lack the ability to perform commonsense reasoning upon facing an unseen situation, mostly due to incapability of identifying a diverse range of implicit social relations.

Question Answering

Keypoint-Aligned Embeddings for Image Retrieval and Re-identification

no code implementations26 Aug 2020 Olga Moskvyak, Frederic Maire, Feras Dayoub, Mahsa Baktashmotlagh

Learning embeddings that are invariant to the pose of the object is crucial in visual image retrieval and re-identification.

Image Retrieval Multi-Task Learning

Adversarial Bipartite Graph Learning for Video Domain Adaptation

1 code implementation31 Jul 2020 Yadan Luo, Zi Huang, Zijian Wang, Zheng Zhang, Mahsa Baktashmotlagh

To further enhance the model capacity and testify the robustness of the proposed architecture on difficult transfer tasks, we extend our model to work in a semi-supervised setting using an additional video-level bipartite graph.

Domain Adaptation Graph Learning +1

Progressive Graph Learning for Open-Set Domain Adaptation

1 code implementation ICML 2020 Yadan Luo, Zijian Wang, Zi Huang, Mahsa Baktashmotlagh

The existing domain adaptation approaches which tackle this problem work in the closed-set setting with the assumption that the source and the target data share exactly the same classes of objects.

Domain Adaptation Graph Learning

Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors

1 code implementation ECCV 2020 Mateusz Michalkiewicz, Sarah Parisot, Stavros Tsogkas, Mahsa Baktashmotlagh, Anders Eriksson, Eugene Belilovsky

In this work we demonstrate experimentally that naive baselines do not apply when the goal is to learn to reconstruct novel objects using very few examples, and that in a \emph{few-shot} learning setting, the network must learn concepts that can be applied to new categories, avoiding rote memorization.

3D Reconstruction Few-Shot Learning +2

Implicitly Defined Layers in Neural Networks

no code implementations3 Mar 2020 Qianggong Zhang, Yanyang Gu, Michalkiewicz Mateusz, Mahsa Baktashmotlagh, Anders Eriksson

In conventional formulations of multilayer feedforward neural networks, the individual layers are customarily defined by explicit functions.

Learning landmark guided embeddings for animal re-identification

no code implementations9 Jan 2020 Olga Moskvyak, Frederic Maire, Feras Dayoub, Mahsa Baktashmotlagh

Our method outperforms the same model without body landmarks input by 26% and 18% on the synthetic and the real datasets respectively.

Person Re-Identification

Correlation-aware Adversarial Domain Adaptation and Generalization

no code implementations29 Nov 2019 Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh, Sridha Sridharan

Domain adaptation (DA) and domain generalization (DG) have emerged as a solution to the domain shift problem where the distribution of the source and target data is different.

Domain Generalization

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

no code implementations12 Nov 2019 Yadan Luo, Zi Huang, Zheng Zhang, Ziwei Wang, Mahsa Baktashmotlagh, Yang Yang

Meta-learning for few-shot learning allows a machine to leverage previously acquired knowledge as a prior, thus improving the performance on novel tasks with only small amounts of data.

Continual Learning Few-Shot Learning

Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant Embeddings

1 code implementation28 Feb 2019 Olga Moskvyak, Frederic Maire, Asia O. Armstrong, Feras Dayoub, Mahsa Baktashmotlagh

We present a novel system for visual re-identification based on unique natural markings that is robust to occlusions, viewpoint and illumination changes.

On Minimum Discrepancy Estimation for Deep Domain Adaptation

no code implementations2 Jan 2019 Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh, Sridha Sridharan

In the presence of large sets of labeled data, Deep Learning (DL) has accomplished extraordinary triumphs in the avenue of computer vision, particularly in object classification and recognition tasks.

Domain Adaptation General Classification +1

Multi-component Image Translation for Deep Domain Generalization

no code implementations21 Dec 2018 Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh, Sridha Sridharan

If DA methods are applied directly to DG by a simple exclusion of the target data from training, poor performance will result for a given task.

Domain Generalization Translation

Learning Factorized Representations for Open-set Domain Adaptation

no code implementations ICLR 2019 Mahsa Baktashmotlagh, Masoud Faraki, Tom Drummond, Mathieu Salzmann

To this end, we rely on the intuition that the source and target samples depicting the known classes can be generated by a shared subspace, whereas the target samples from unknown classes come from a different, private subspace.

Domain Adaptation

On Encoding Temporal Evolution for Real-time Action Prediction

no code implementations22 Sep 2017 Fahimeh Rezazadegan, Sareh Shirazi, Mahsa Baktashmotlagh, Larry S. Davis

Anticipating future actions is a key component of intelligence, specifically when it applies to real-time systems, such as robots or autonomous cars.

From Review to Rating: Exploring Dependency Measures for Text Classification

no code implementations4 Sep 2017 Samuel Cunningham-Nelson, Mahsa Baktashmotlagh, Wageeh Boles

In this work, we explore using statistical dependence measures for textual classification, representing text as word vectors.

Classification General Classification +2

Domain Adaptation on the Statistical Manifold

no code implementations CVPR 2014 Mahsa Baktashmotlagh, Mehrtash T. Harandi, Brian C. Lovell, Mathieu Salzmann

Here, we propose to make better use of the structure of this manifold and rely on the distance on the manifold to compare the source and target distributions.

Object Recognition Unsupervised Domain Adaptation

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