Svetlana Lazebnik

About

Svetlana Lazebnik is a Professor and Willett Faculty Scholar at the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign. Her research areas include Artificial Intelligence, with recent courses taught related to Deep Learning for Computer Vision and introductory deep learning courses. She is recognized for her contributions to computer vision, as evidenced by her honors such as being named a University Scholar and her recognition as a distinguished speaker discussing generative image models for virtual try-on and stylization. Lazebnik's work focuses on advancing understanding and applications in computer vision, contributing to the academic community through teaching and research.

Research topics

• Natural Language Processing
• Artificial Intelligence
• Computer Science
• Theoretical computer science

Selected publications

• Contextual Translation Embedding for Visual Relationship Detection and Scene Graph Generation

  IEEE Transactions on Pattern Analysis and Machine Intelligence · 2020 · 86 citations

  Senior authorCorresponding

  Relations amongst entities play a central role in image understanding. Due to the complexity of modeling (subject, predicate, object) relation triplets, it is crucial to develop a method that can not only recognize seen relations, but also generalize to unseen cases. Inspired by a previously proposed visual translation embedding model, or VTransE [1] , we propose a context-augmented translation embedding model that can capture both common and rare relations. The previous VTransE model maps entit…

  DOI

• Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing

  2021 IEEE/CVF International Conference on Computer Vision (ICCV) · 2021-10-01 · 58 citations

  articleSenior author

  We proposes a flexible person generation framework called Dressing in Order (DiOr), which supports 2D pose transfer, virtual try-on, and several fashion editing tasks. The key to DiOr is a novel recurrent generation pipeline to sequentially put garments on a person, so that trying on the same garments in different orders will result in different looks. Our system can produce dressing effects not achievable by existing work, including different interactions of garments (e.g., wearing a top tucked…

  PublisherDOI

• ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs

  Lecture notes in computer science · 2024-09-29 · 41 citations

  book-chapter
  PublisherDOI

• Shadows Don't Lie and Lines Can't Bend! Generative Models Don't know Projective Geometry…for Now

  2024-06-16 · 24 citations

  article

  Generative models can produce impressively realistic images. This paper demonstrates that generated images have geometric features different from those of real images. We build a set of collections of generated images, prequalified to fool simple, signal-based classifiers into believing they are real. We then show that prequalified generated images can be identified reliably by classifiers that only look at geometric properties. We use three such classifiers. All three classifiers are denied acc…

  PublisherDOI

• GridToPix: Training Embodied Agents with Minimal Supervision

  2021 IEEE/CVF International Conference on Computer Vision (ICCV) · 2021-10-01 · 12 citations

  article

  While deep reinforcement learning (RL) promises freedom from hand-labeled data, great successes, especially for Embodied AI, require significant work to create supervision via carefully shaped rewards. Indeed, without shaped rewards, i.e., with only terminal rewards, present-day Embodied AI results degrade significantly across Embodied AI problems from single-agent Habitat-based PointGoal Navigation (SPL drops from 55 to 0) and two-agent AI2-THOR-based Furniture Moving (success drops from 58% to…

  PublisherDOI

Recent grants

• CAREER: Similarity-based Representation of Large-scale Image Collections

  NSF · $372k · 2012–2015

• RI: Medium: Collaborative Research: Text-to-Image Reference Resolution for Image Understanding and Manipulation

  NSF · $550k · 2016–2021

Frequent coauthors

• Cordelia Schmid

  40 shared

• Jean Ponce

  Département d'Informatique

  31 shared

• Arun Mallya

  Nvidia (United States)

  25 shared

• Alexander G. Schwing

  24 shared

• Bryan A. Plummer

  20 shared

• C. Schmid

  18 shared

• Unnat Jain

  Carnegie Mellon University

  18 shared

• Andrew Fitzgibbon

  16 shared

Labs

• Siebel School of Computing and Data SciencePI

Education

• Ph.D., Computer Science

  University of California, Berkeley

  1999

• M.S., Computer Science

  University of California, Berkeley

  1994

• B.S., Computer Science

  University of Illinois at Urbana-Champaign

  1991

Awards & honors

• University Scholar Honor

Similar researchers at University of Illinois Urbana-Champaign

• Jiawei Hanh-146
• Kaiyu Guanh-86
• ChengXiang Zhaih-83