Amir Rahimi
Postdoctoral Research Fellow, University of Michigan
amirr [AT] umich.edu
About
I am a postdoctoral research fellow at the University of Michigan working with Stella Yu. Previously, I was a postdoctoral researcher at MIT department of Brain & Cognitive Sciences. I obtained PhD from the College of Engineering and Computer Science at the Australian National University. I was furtunate to be advised by Prof. Richard Hartley. I received my bachelor's and master's degrees from the University of Tehran. My research interests lie in the fields of Computer Vision and Machine Learning. Specifically, I work on problems with limited data/supervision, data diversity design for out-of-distribution systematic generalization, deep neural network confidence calibration, and inference in probabilistic graphical models.
Recent Publications
‡ indicates equal contribution.
D3: Data Diversity Design for Systematic Generalization in Visual Question Answering
Amir Rahimi, Vanessa D'Amario, Moyuru Yamada, Kentaro Takemoto, Tomotake Sasaki, Xavier Boix
Transactions on Machine Learning Research, (TMLR 2024).
Few-shot Weakly-Supervised Object Detection via Directional Statistics
Amirreza Shaban‡, Amir Rahimi‡, Thalaiyasingam Ajanthan, Byron Boots, Richard Hartley
Winter Conference on Applications of Computer Vision, (WACV 2022), Waikoloa, HI.
Semi-Supervised 3D Hand Shape and Pose Estimation with Label Propagation (Oral & Award Candidate)
Samira Kaviani, Amir Rahimi, Richard Hartley
Digital Image Computing: Techniques and Applications (DICTA 2021), Gold Coast, QLD, Australia.
Calibration of neural networks using splines
Kartik Gupta, Amir Rahimi, Thalaiyasingam Ajanthan, Thomas Mensink, Cristian Sminchisescu, Richard Hartley
International Conference on Learning Representation, (ICLR 2021), Virtual Conference (formerly Vienna, Austria).
Post-hoc Calibration of Neural Networks
Amir Rahimi, Kartik Gupta, Thalaiyasingam Ajanthan, Thomas Mensink, Cristian Sminchisescu, Richard Hartley
Arxiv preprint, 2020
Pairwise similarity knowledge transfer for weakly supervised object localization
Amir Rahimi‡, Amirreza Shaban‡, Thalaiyasingam Ajanthan, Richard Hartley, Byron Boots
European Conference on Computer Vision, (ECCV 2020), Virtual Conference (formerly Glasgow, Scotland).
Intra order-preserving functions for calibration of multi-class neural networks
Amir Rahimi‡, Amirreza Shaban‡, Ching-An Cheng‡, Richard Hartley, Byron Boots
34th Conference on Neural Information Processing Systems (NeurIPS 2020), Virtual Conference (formerly Vancouver, Canada).
Learning to find common objects across few image collections
Amirreza Shaban‡, Amir Rahimi‡, Shray Bansal, Stephen Gould, Byron Boots, Richard Hartley
Proceedings of the IEEE/CVF International Conference on Computer Vision, (ICCV 2019), Seoul, Korea.