activity
20182022
most citedNo-Reference Image Quality Assessment via Feature Fusion and Multi-Task Learning

13 citations · 27 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV20222 cited

Unified Fully and Timestamp Supervised Temporal Action Segmentation via Sequence to Sequence Translation

Nadine Behrmann, S. Alireza Golestaneh, Zico Kolter +2

This paper introduces a unified framework for video action segmentation via sequence to sequence (seq2seq) translation in a fully and timestamp supervised setup. In contrast to cur…

cs.CV20202 cited

Importance of Self-Consistency in Active Learning for Semantic Segmentation

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 pe…

cs.CV202010 cited

3D Human Motion Estimation via Motion Compression and Refinement

Zhengyi Luo, S. Alireza Golestaneh, Kris M. Kitani

We develop a technique for generating smooth and accurate 3D human pose and motion estimates from RGB video sequences. Our method, which we call Motion Estimation via Variational A…

cs.CV202013 cited

No-Reference Image Quality Assessment via Feature Fusion and Multi-Task Learning

S. Alireza Golestaneh, Kris Kitani

Blind or no-reference image quality assessment (NR-IQA) is a fundamental, unsolved, and yet challenging problem due to the unavailability of a reference image. It is vital to the s…

cs.CV2018

Synthesized Texture Quality Assessment via Multi-scale Spatial and Statistical Texture Attributes of Image and Gradient Magnitude Coefficients

S. Alireza Golestaneh, Lina Karam

Perceptual quality assessment for synthesized textures is a challenging task. In this paper, we propose a training-free reduced-reference (RR) objective quality assessment method t…