Hosnieh Sattar (PhD Student)

MSc Hosnieh Sattar

Address
Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus E1 4
66123 Saarbrücken
Location
E1 4 - Room 626
Phone
+49 681 9325 1208
Fax
+49 681 9325 2099
Email
Get email via email

Personal Information

 

Research Interests

  • Machine Learning and Pattern Recognition
  • Eye Tracking and Visual Cognition
  • Image Analysis and Computer vision
  • Human-Computer Interaction

 

Education

  • 2015–present, Ph.D. student in Computer Science, Max Planck Institute for Informatics
  • 2014, M.Sc. in Visual Computing, Saarland University
  • 2011, B.Sc. in Biomedical Engineering, Islamic Azad University of Mashhad

Teaching

  • PDE and Boundary Value Problems, Saarland University, (Dr. Darya Apushkinskaya, 2013/14)

Research Projects

 

Publications

2017
Predicting the Category and Attributes of Mental Pictures Using Deep Gaze Pooling
H. Sattar, A. Bulling and M. Fritz
Mutual Benefits of Cognitive and Computer Vision (MBCC @ICCV 2017), 2017
(Accepted/in press)
Abstract
Previous work focused on predicting visual search targets from human fixations but, in the real world, a specific target is often not known, e.g. when searching for a present for a friend. In this work we instead study the problem of predicting the mental picture, i.e. only an abstract idea instead of a specific target. This task is significantly more challenging given that mental pictures of the same target category can vary widely depending on personal biases, and given that characteristic target attributes can often not be verbalised explicitly. We instead propose to use gaze information as implicit information on users' mental picture and present a novel gaze pooling layer to seamlessly integrate semantic and localized fixation information into a deep image representation. We show that we can robustly predict both the mental picture's category as well as attributes on a novel dataset containing fixation data of 14 users searching for targets on a subset of the DeepFahion dataset. Our results have important implications for future search interfaces and suggest deep gaze pooling as a general-purpose approach for gaze-supported computer vision systems.
Visual Decoding of Targets During Visual Search From Human Eye Fixations
H. Sattar, M. Fritz and A. Bulling
Technical Report, 2017
(arXiv: 1706.05993)
Abstract
What does human gaze reveal about a users' intents and to which extend can these intents be inferred or even visualized? Gaze was proposed as an implicit source of information to predict the target of visual search and, more recently, to predict the object class and attributes of the search target. In this work, we go one step further and investigate the feasibility of combining recent advances in encoding human gaze information using deep convolutional neural networks with the power of generative image models to visually decode, i.e. create a visual representation of, the search target. Such visual decoding is challenging for two reasons: 1) the search target only resides in the user's mind as a subjective visual pattern, and can most often not even be described verbally by the person, and 2) it is, as of yet, unclear if gaze fixations contain sufficient information for this task at all. We show, for the first time, that visual representations of search targets can indeed be decoded only from human gaze fixations. We propose to first encode fixations into a semantic representation and then decode this representation into an image. We evaluate our method on a recent gaze dataset of 14 participants searching for clothing in image collages and validate the model's predictions using two human studies. Our results show that 62% (Chance level = 10%) of the time users were able to select the categories of the decoded image right. In our second studies we show the importance of a local gaze encoding for decoding visual search targets of user
2015
Prediction of Search Targets from Fixations in Open-world Settings
H. Sattar, S. Müller, M. Fritz and A. Bulling
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), 2015