Anna Penzkofer

Institute for Visualisation and Interactive Systems, University of Stuttgart

  Pfaffenwaldring 5a, 70569 Stuttgart, Germany University of Stuttgart, SimTech Building, Room 01.033 Google Scholar

Biography

Anna Penzkofer is a PhD student in the Perceptual User Interfaces group since January 2023. She holds a Bachelor’s degree in Computer Science from the University of Stuttgart and a Master’s degree in Computer Science from the Technical University Munich. She has been doing research in the Perceptual User Interfaces group since her bachelor's degree at the University of Stuttgart in 2018. Her main research interest is in Reinforcement Learning and Perception with a focus on Computational Rationality and Interactive Behaviour. Her passion is building connections with other people and creating something new.

Personal website https://apenzko.github.io/

Teaching

2023

Mensch-Computer-Interaktion (Teaching Assistant)Bachelor

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Supervision

2023

Evaluation of Different Image Representations for Reinforcement Learning Agents (M.Sc.) Jayakumar Ramasamy Sundararaj*

 

Biologically Plausible Reinforcement Learning (B.Sc.) Tobias Stegmaier

 

An Adaptive Model of Gaze-based Selection (INFOTECH project) Jayakumar Ramasamy Sundararaj

*Co-supervised with Constantin Ruhdorfer

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Open Thesis Projects

 

Cognitive Attention Guided Imitation LearningMaster

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Publications

(Penzkofer et al., 2024; Penzkofer et al., 2023; Penzkofer et al., 2021)

1. VSA4VQA: Scaling A Vector Symbolic Architecture To Visual Question Answering on Natural Images

   Anna Penzkofer, Lei Shi, Andreas Bulling

   Proc. 46th Annual Meeting of the Cognitive Science Society (CogSci), pp. , 2024.

   Abstract BibTeX Project

   While Vector Symbolic Architectures (VSAs) are promising for modelling spatial cognition, their application is currently limited to artificially generated images and simple spatial queries. We propose VSA4VQA – a novel 4D implementation of VSAs that implements a mental representation of natural images for the challenging task of Visual Question Answering (VQA). VSA4VQA is the first model to scale a VSA to complex spatial reasoning. Our method is based on the Semantic Pointer Architecture (SPA) to encode objects in a hyperdimensional vector space. To encode natural images, we extend the SPA to include dimensions for object’s width and height in addition to their spatial location. To perform spatial queries we further introduce learned spatial query masks and integrate a pre-trained vision-language model for answering attribute-related questions. We evaluate our method on the GQA benchmark dataset and show that it can effectively encode natural images, achieving competitive performance to state-of-the-art deep learning methods for zero-shot VQA.

   @inproceedings{penzkofer24_cogsci, author = {Penzkofer, Anna and Shi, Lei and Bulling, Andreas}, title = {VSA4VQA: Scaling A Vector Symbolic Architecture To Visual Question Answering on Natural Images}, booktitle = {Proc. 46th Annual Meeting of the Cognitive Science Society (CogSci)}, year = {2024}, pages = {} }

1. 

   Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning

   Anna Penzkofer, Simon Schaefer, Florian Strohm, Mihai Bâce, Stefan Leutenegger, Andreas Bulling

   Proc. Adaptive and Learning Agents Workshop (ALA), pp. 1–7, 2023.

   Abstract Links BibTeX Project

   While deep reinforcement learning (RL) agents outperform humans on an increasing number of tasks, training them requires data equivalent to decades of human gameplay. Recent hierarchical RL methods have increased sample efficiency by incorporating information inherent to the structure of the decision problem but at the cost of having to discover or use human-annotated sub-goals that guide the learning process. We show that intentions of human players, i.e. the precursor of goal-oriented decisions, can be robustly predicted from eye gaze even for the long-horizon sparse rewards task of Montezuma’s Revenge – one of the most challenging RL tasks in the Atari2600 game suite. We propose Int-HRL: Hierarchical RL with intention-based sub-goals that are inferred from human eye gaze. Our novel sub-goal extraction pipeline is fully automatic and replaces the need for manual sub-goal annotation by human experts. Our evaluations show that replacing hand-crafted sub-goals with automatically extracted intentions leads to a HRL agent that is significantly more sample efficient than previous methods.

   Paper: penzkofer23_ala.pdf

   @inproceedings{penzkofer23_ala, author = {Penzkofer, Anna and Schaefer, Simon and Strohm, Florian and Bâce, Mihai and Leutenegger, Stefan and Bulling, Andreas}, title = {Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning}, booktitle = {Proc. Adaptive and Learning Agents Workshop (ALA)}, year = {2023}, pages = {1--7} }

1. 

   ConAn: A Usable Tool for Multimodal Conversation Analysis

   Anna Penzkofer, Philipp Müller, Felix Bühler, Sven Mayer, Andreas Bulling

   Proc. ACM International Conference on Multimodal Interaction (ICMI), pp. 341-351, 2021.

   Abstract Links BibTeX Project

   Multimodal analysis of group behavior is a key task in human-computer interaction, as well as the social and behavioral sciences, but is often limited to more easily controllable laboratory settings or requires elaborate multi-sensor setups and time-consuming manual data annotation. We present ConAn – a usable tool to explore and automatically analyze non-verbal behavior of multiple persons during natural group conversations. In contrast to traditional multi-sensor setups, our tool only requires a single 360° camera and uses state-of-the-art computer vision methods to automatically extract behavioral indicators, such as gaze direction, facial expressions, and speaking activity. Thus, our tool allows for easy and fast deployment supporting researchers in understanding both individual behavior and group interaction dynamics, but also in quantifying user-object interactions. We illustrate the benefits of our tool on three sample use cases: general conversation analysis, assessment of collaboration quality, and impact of technology on audience behavior. Taken together, ConAn represents an important step towards democratizing automatic conversation analysis in HCI and beyond.

   doi: 10.1145/3462244.3479886

   Paper: penzkofer21_icmi.pdf

   Code: https://git.hcics.simtech.uni-stuttgart.de/public-projects/conan

   Video: https://www.youtube.com/watch?v=H2KfZNgx6CQ

   @inproceedings{penzkofer21_icmi, author = {Penzkofer, Anna and Müller, Philipp and Bühler, Felix and Mayer, Sven and Bulling, Andreas}, title = {ConAn: A Usable Tool for Multimodal Conversation Analysis}, booktitle = {Proc. ACM International Conference on Multimodal Interaction (ICMI)}, year = {2021}, doi = {10.1145/3462244.3479886}, pages = {341-351}, video = {https://www.youtube.com/watch?v=H2KfZNgx6CQ} }