2 code implementations • 1 Sep 2022 • Nadine Behrmann, S. Alireza Golestaneh, Zico Kolter, Juergen Gall, Mehdi Noroozi
This paper introduces a unified framework for video action segmentation via sequence to sequence (seq2seq) translation in a fully and timestamp supervised setup.
Ranked #4 on Action Segmentation on Assembly101
no code implementations • 16 Dec 2021 • Akash Umakantha, Joao D. Semedo, S. Alireza Golestaneh, Wan-Yi S. Lin
In this work, we empirical evaluated how different data augmentation strategies performed on CNN (e. g., ResNet) versus ViT architectures for image classification.
1 code implementation • 16 Aug 2021 • S. Alireza Golestaneh, Saba Dadsetan, Kris M. Kitani
Specifically, we enforce self-consistency between the outputs of our quality assessment model for each image and its transformation (horizontally flipped) to utilize the rich self-supervisory information and reduce the uncertainty of the model.
Ranked #3 on No-Reference Image Quality Assessment on TID2013
2 code implementations • 9 Aug 2020 • Zhengyi Luo, S. Alireza Golestaneh, Kris M. Kitani
Experiments show that our method produces both smooth and accurate 3D human pose and motion estimates.
Ranked #15 on 3D Human Pose Estimation on 3DPW (Acceleration Error metric, using extra training data)
no code implementations • 4 Aug 2020 • S. Alireza Golestaneh, Kris M. Kitani
We address the task of active learning in the context of semantic segmentation and show that self-consistency can be a powerful source of self-supervision to greatly improve the performance of a data-driven model with access to only a small amount of labeled data.
no code implementations • 6 Jun 2020 • S. Alireza Golestaneh, Kris Kitani
In our experiments, we demonstrate that by utilizing multi-task learning and our proposed feature fusion method, our model yields better performance for the NR-IQA task.
Multi-Task Learning No-Reference Image Quality Assessment +1
no code implementations • 21 Apr 2018 • S. Alireza Golestaneh, Lina Karam
Performance evaluations on two synthesized texture databases demonstrate that our proposed RR synthesized texture quality metric significantly outperforms both full-reference and RR state-of-the-art quality metrics in predicting the perceived visual quality of the synthesized textures.
1 code implementation • CVPR 2017 • S. Alireza Golestaneh, Lina J. Karam
The detection of spatially-varying blur without having any information about the blur type is a challenging task.