Yiannis Aloimonos

About

Yiannis Aloimonos is a professor in the Department of Computer Science, UMIACS, ISR, MRC, and NACS. His research focuses on areas related to computer science, with an emphasis on advancing knowledge and techniques within these fields. As a faculty member at the University of Maryland, he contributes to the academic community through teaching, research, and collaboration, supporting initiatives such as AIM and engaging in efforts to enhance career readiness through training programs. His work is integral to the development of innovative solutions in computer science and related disciplines.

Research topics

• Artificial Intelligence
• Computer Science
• Theoretical computer science
• Human–computer interaction
• Psychology
• Cognitive psychology
• Computer vision
• Cognitive science

Selected publications

• Learning Visual Motion Segmentation Using Event Surfaces

  2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2020 · 63 citations

  Senior authorCorresponding

  Event-based cameras have been designed for scene motion perception - their high temporal resolution and spatial data sparsity converts the scene into a volume of boundary trajectories and allows to track and analyze the evolution of the scene in time. Analyzing this data is computationally expensive, and there is substantial lack of theory on dense-in-time object motion to guide the development of new algorithms; hence, many works resort to a simple solution of discretizing the event stream and…

  DOI

• Forecasting Action Through Contact Representations From First Person Video

  IEEE Transactions on Pattern Analysis and Machine Intelligence · 2021 · 52 citations

  Senior authorCorresponding

  Human actions involving hand manipulations are structured according to the making and breaking of hand-object contact, and human visual understanding of action is reliant on anticipation of contact as is demonstrated by pioneering work in cognitive science. Taking inspiration from this, we introduce representations and models centered on contact, which we then use in action prediction and anticipation. We annotate a subset of the EPIC Kitchens dataset to include time-to-contact between hands and…

  DOI

• Repurposing Pre-trained Video Diffusion Models for Event-based Video Interpolation

  2025-06-10 · 2 citations

  articleSenior author

  Video Frame Interpolation aims to recover realistic missing frames between observed frames, generating a high-frame-rate video from a low-frame-rate video. However, without additional guidance, the large motion between frames makes this problem ill-posed. Event-based Video Frame Interpolation (EVFI) addresses this challenge by using sparse, high-temporal-resolution event measurements as motion guidance. This guidance allows EVFI methods to significantly outperform frame-only methods. However, to…

  PublisherDOI

• ViewActive: Active viewpoint optimization from a single image

  2025-10-19 · 1 citations

  articleSenior author

  When observing objects, humans benefit from their spatial visualization and mental rotation ability to envision potential optimal viewpoints based on the current observation. This capability is crucial for enabling robots to achieve efficient and robust scene perception during operation, as optimal viewpoints provide essential and informative features for accurately representing scenes in 2D images, thereby enhancing downstream tasks.To endow robots with this human-like active viewpoint optimiza…

  PublisherDOI

• Discovering Object Attributes by Prompting Large Language Models With Perception-Action Apis

  2025-05-19 · 1 citations

  articleSenior author

  There has been a lot of interest in grounding natural language to physical entities through visual context. While Vision Language Models (VLMs) can ground linguistic instructions to visual sensory information, they struggle with grounding non-visual attributes, like the weight of an object. Our key insight is that non-visual attribute detection can be effectively achieved by active perception guided by visual reasoning. To this end, we present a perception-action API that consists of VLMs and La…

  PublisherDOI

Frequent coauthors

• Cornelia Fermüller

  232 shared

• Chahat Deep Singh

  40 shared

• Nitin J. Sanket

  39 shared

• Yezhou Yang

  Arizona State University

  30 shared

• Chethan M. Parameshwara

  23 shared

• Patrick Baker

  Pointwise (United States)

  15 shared

• Ching L. Teo

  University of Maryland, College Park

  15 shared

• Xiaomin Lin

  15 shared

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