1 code implementation • 28 Mar 2023 • Hojjat Mokhtarabadi, Kave Bahraman, Mehrdad Hosseinzadeh, Mahdi Eftekhari
Empirical evaluations on correlation-based metrics, such as Kendall's $\tau$ and Spearman's $\rho$ demonstrate the superiority of our approach compared to existing state-of-the-art methods in assigning relative scores to the input frames.
no code implementations • 18 Oct 2022 • Yong Wu, Shekhor Chanda, Mehrdad Hosseinzadeh, Zhi Liu, Yang Wang
In this paper, we propose task-specific meta distillation that simultaneously learns two models in meta-learning: a large teacher model and a small student model.
no code implementations • CVPR 2021 • Mehrdad Hosseinzadeh, Yang Wang
Inspired by the success of multi-task learning, we formulate a training scheme that uses an auxiliary task to improve the training of the change captioning network.
no code implementations • CVPR 2020 • Mehrdad Hosseinzadeh, Yang Wang
The goal is then to retrieve the images that are generally similar to the query image, but differ according to the requested modification.
no code implementations • 17 Jan 2020 • Seyed Shahabeddin Nabavi, Mehrdad Hosseinzadeh, Ramin Fahimi, Yang Wang
We consider the problem of unsupervised camera pose estimation.
no code implementations • 18 Sep 2019 • Nader Asadi, Amir M. Sarfi, Mehrdad Hosseinzadeh, Zahra Karimpour, Mahdi Eftekhari
In this work, we propose a learning framework to improve the shape bias property of self-supervised methods.
Ranked #51 on Domain Generalization on PACS
no code implementations • 1 Jul 2019 • Nader Asadi, AmirMohammad Sarfi, Mehrdad Hosseinzadeh, Sahba Tahsini, Mahdi Eftekhari
Our method can be applied to any layer of any arbitrary model without the need of any modification or additional training.
no code implementations • 5 Mar 2019 • Mohammad Asiful Hossain, Mehrdad Hosseinzadeh, Omit Chanda, Yang Wang
In this paper, we consider the problem of crowd counting in images.