Krista Ehinger
Scholar

Krista Ehinger

Google Scholar ID: EdGfpdcAAAAJ
School of Computing and Information Systems, The University of Melbourne
visioncomputer visionartificial intelligence
Homepage↗Google Scholar↗
Citations & Impact
All-time
Citations
5,499
 
H-index
17
 
i10-index
23
 
Publications
20
 
Co-authors
0
 
Contact
CVOpen ↗
Resume
Academic Achievements
  • 2025: 'Deep Learning and Geometric Modeling for 3D Reconstruction of Subsurface Utilities from GPR Data' in Sensors
  • 2025: 'Tests of a Hybrid-Similarity Exemplar Model of Context-Dependent Memorability in a High-Dimensional Real-World Category Domain' in Journal of Experimental Psychology: General
  • 2025: 'Reasoning Like Experts: Leveraging Multimodal Large Language Models for Drawing-based Psychoanalysis' at ACM Multimedia
  • 2025: 'Do Explanations Expose Bias? How Saliency Maps Affect Judgements of Biased Face-Recognition Models' at ECAI
  • 2025: 'Planning-Driven Programming: A Large Language Model Programming Workflow' at ACL
  • 2025: 'Beyond Perception: Evaluating Abstract Visual Reasoning through Multi-Stage Task' in ACL Findings
  • 2025: 'Open-World Amodal Appearance Completion' at CVPR
  • 2025: 'An equivalent illuminant analysis of lightness constancy with physical objects and in virtual reality' in Behavior Research Methods
  • 2025: 'Do we need watchful eyes on our workers? Ethics of using computer vision for workplace surveillance' in AI Ethics
  • 2025: Co-authored 'Improved Level Set Method for Particle Reconstruction from X-Ray Computed Tomography Images'
Background
  • Associate Professor and co-lead of the AI group in the School of Computing and Information Systems at the University of Melbourne
  • Research focuses on the intersection of human and computer vision, including scene recognition, visual search, and depth perception in natural scenes
  • Develops computer vision algorithms for place recognition and navigation, using scene context to support object detection and recognition
  • Interested in how these processes occur in the human visual system
  • Combines computational modeling (e.g., Bayesian models, deep neural networks) with behavioral methods (e.g., psychophysics, eye tracking, large-scale online experiments)