Ekaterina Potapova

About

Ekaterina Potapova’s research lies at the intersection of computer vision, robotics, and cognitive science, with a core focus on developing attention-driven systems for object detection, segmentation, and manipulation in cluttered environments. Her work is distinguished by pioneering the integration of 3D visual attention models—a relatively underexplored area—into robotic perception, enabling robots to efficiently prioritize and process relevant objects in real time. Her most cited paper, "Learning What Matters: Combining Probabilistic Models of 2D and 3D Saliency Cues" (2011, 37 citations), established a foundational framework for fusing multi-dimensional saliency cues. She further advanced the field with "Attention-driven object detection and segmentation of cluttered table scenes using 2.5D symmetry" (2014, 22 citations), which directly addressed the practical challenge of robotic grasping in domestic settings. Potapova also contributed a comprehensive "Survey of recent advances in 3D visual attention for robotics" (2017, 13 citations), synthesizing key developments for the community. Her notable achievements include bridging visual search with natural language processing for incremental referent grounding, as seen in her 2013 work, and organizing the "Workshop on attention models in robotics" (2014). With a total of over 100 citations, her research has significantly shaped how robots perceive and interact with complex, real-world scenes.

Research Focus

Key Achievements

Top Papers

1. 1
   Learning What Matters: Combining Probabilistic Models of 2D and 3D Saliency Cues

   37 citations · 2011
2. 2
   Attention-driven object detection and segmentation of cluttered table scenes using 2.5D symmetry

   22 citations · 2014
3. 3
   Survey of recent advances in 3D visual attention for robotics

   13 citations · 2017
4. 4
   Incremental attention-driven object segmentation

   10 citations · 2014
5. 5
   Incrementally biasing visual search using natural language input

   7 citations · 2013
6. 6
   Attention-driven segmentation of cluttered 3D scenes

   6 citations · 2012
7. 7
   Local 3D Symmetry for Visual Saliency in 2.5D Point Clouds

   5 citations · 2013
8. 8
   Attention driven grasping for clearing a heap of objects

   4 citations · 2012
9. 9
   Incremental Referent Grounding with NLP-Biased Visual Search

   3 citations · 2012
10. 10
    Workshop on attention models in robotics

    1 citations · 2014

Key Collaborators

Contact & Links