Fatemeh Saleh


Post-doctoral Research Fellow


           

profile photo

Bio: I am a Research Fellow of the Australian Centre for Robotic Vision (ACRV) and the Australian National University (ANU), where I am honoured to work with Professor Stephen Gould. Prior to that, I was a PhD student at Australian National University, working on weakly-supervised semantic segmentation of images and videos under supervisions of Lars Petersson, Mathieu Salzmann, Jose M. Alvarez, and Stephen Gould. I was also a deep learning research engineering intern at Qualcomm AI Research, Amsterdam, working on self-supervised video representation learning.

Recently, I'm working on finding efficient and effective solutions for problems related to modeling human motion and behaviour, such as multiple object tracking, human motion prediction, diverse sequence generation, and action forecasting, and social activity analysis.

Over the past few years, I served the community as a peer reviewer for CVPR, ICCV, ECCV, IJCV, AAAI, Deep Vision (CVPRW), Autonomous Driving (CVPRW), CVRSUAD (ECCVW and ICCVW), ICIP, and ICPR.


Latest News

One paper accepted in ECCV'20. Congradulations to Mahsa.
Our oral CVPR 2020 paper, UC-Net, has been nominated in the finalists of CVPR 2020 paper awards!
Two papers accepted in CVPR'20 (one oral). Congradulations to Sadegh and Jing.
One paper accepted in WACV'20. Congradulations to Cristian Rodriguez.
From January 2019, I joined ANU/ACRV as a Research Fellow under supervision of Prof. Stephen Gould.
After wonderful 3.5 years, I just finished my PhD at ANU! Please find my thesis here.

Research

I'm interested in computer vision and machine learning. My recent research focuses on analysing human(s) in sequences, including but not limited to diverse human motion prediction, multiple human tracking, social activity analysis, and action forecasting. I've also done research on weakly-supervised semantic segmentation during my PhD (see my PhD thesis).

The IKEA ASM Dataset: Understanding People Assembling Furniture through Actions, Objects and Pose
Yizhak Ben-Shabat, Xin Yu, Fatemeh Saleh, Dylan Campbell, Cristian Rodriguez-Opazo, Hongdong Li, Stephen Gould
Winter Conference on Applications of Computer Vision (WACV), 2021
[project page / arXiv / Dataset / code]
Joint learning of Social Groups, Individuals Action and Sub-group Activities in Videos
Mahsa Ehsanpour, Alireza Abedin, Fatemeh Saleh, Javen Shi, Ian Reid, Hamid Rezatofighi
European Conference on Computer Vision (ECCV), 2020
[project page / arXiv / video / code]
ArTIST: Autoregressive Trajectory Inpainting and Scoring for Tracking
Fatemeh Saleh, Sadegh Aliakbarian, Mathieu Salzmann, Stephen Gould
ArXiv, 2020
[project page / arXiv / video / code]
A Stochastic Conditioning Scheme for Diverse Human Motion Prediction
Sadegh Aliakbarian, Fatemeh Saleh, Mathieu Salzmann, Lars Petersson, Stephen Gould
Proceedings of the IEEE international conference on computer vision (CVPR), 2020
[project page / arXiv / video / code]
UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
Jing Zhang, Deng-Ping Fan, Yuchao Dai, Saeed Anwar, Fatemeh Saleh, Tong Zhang, Nick Barnes
Proceedings of the IEEE international conference on computer vision (CVPR), 2020 (Oral presentation)
[project page / arXiv / video / code]
Proposal-free Temporal Moment Localization of a Natural-Language Query in Video using Guided Attention
Cristian Rodriguez Opazo, Edison Marrese-Taylor, Fatemeh Saleh, Hongdong Li, Stephen Gould
Winter Conference on Applications of Computer Vision (WACV), 2020
[project page / arXiv / Video / Code]
Sampling Good Latent Variables via CPP-VAEs: VAEs with Condition Posterior as Prior
Sadegh Aliakbarian, Fatemeh Saleh, Mathieu Salzmann, Lars Petersson, Stephen Gould
ArXiv, 2019
[project page / arXiv / Video / Code]
VIENA2: A Driving Anticipation Dataset
Sadegh Aliakbarian, Fatemeh Saleh, Mathieu Salzmann, Basura Fernando, Lars Petersson, Lars Andersson
Asian Conference on Computer Vision (ACCV), 2018
[project page / arXiv / Video / Code]
Effective Use of Synthetic Data in Urban Scene Semantic Segmentation
Fatemeh Saleh, Sadegh Aliakbarian, Mathieu Salzmann, Lars Petersson, Jose M Alvarez
European Conference on Computer Vision (ECCV), 2018
[project page / arXiv / Video / Code]
Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation
Fatemeh Saleh, Sadegh Aliakbarian, Mathieu Salzmann, Lars Petersson, Jose M Alvarez, Stephen Gould
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2018
[project page / arXiv / Video / Code]
Encouraging LSTMs to Anticipate Actions Very Early
Sadegh Aliakbarian, Fatemeh Saleh, Mathieu Salzmann, Basura Fernando, Lars Petersson, Lars Andersson
International Conference on Computer Vision (ICCV), 2017
[project page / arXiv / Video / Code]
Bringing background into the foreground: Making all classes equal in weakly-supervised video semantic segmentation
Fatemeh Saleh, Sadegh Aliakbarian, Mathieu Salzmann, Lars Petersson, Jose M Alvarez
International Conference on Computer Vision (ICCV), 2017
[project page / arXiv / Video / Code]
Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation
Fatemeh Saleh, Sadegh Aliakbarian, Mathieu Salzmann, Lars Petersson, Stephen Gould, Jose M Alvarez
European Conference on Computer Vision (ECCV), 2016
[project page / arXiv / Video / Code]
Recent Experiences
Research Fellow | Australian Centre for Robotic Vision (ACRV), Canberra, Australia
[January 2019 - ~ December 2020]
As a research fellow at ACRV, I am working on both academic and industrial projects in computer vision and machine learning. My research mostly focuses on analysing human motion and behaviour, e.g., multiple human tracking, diverse motion prediction, social activity analysis, and action forecasting.
Machine Learning Research Consultant | Joint ACRV/LumaChain collaboration
[June 2020 - August 2020]
As a research consultant, we are delivering multiple (deformable and visually similar) object tracking in highly occluded scenarios. The project is related to the real-world challenging scenarios of food chain industry.
Research Intern | Qualcomm AI Research, Amsterdam, Netherlands
[May 2018 - October 2018]
As a research intern at Qualcomm AI Research, I was working on self-supervised representation learning for video analysis tasks.
Education
PhD, Computer Science, Australian National University, [June 2015 - February 2019]
MSc, Artificial Intelligence, Sharif University of Technology, [October 2011 - September 2013]
BSc, Computer Software Engineering, Isfahan University of Technology, [October 2007 - September 2011]

 

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