NODIS: Neural Ordinary Differential Scene Understanding

Yuren Cong, Hanno Ackermann, Wentong Liao, Michael Ying Yang, Bodo Rosenhahn

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

7 Citations (SciVal)

Abstract

Semantic image understanding is a challenging topic in computer vision. It requires to detect all objects in an image, but also to identify all the relations between them. Detected objects, their labels and the discovered relations can be used to construct a scene graph which provides an abstract semantic interpretation of an image. In previous works, relations were identified by solving an assignment problem formulated as (Mixed-)Integer Linear Programs. In this work, we interpret that formulation as Ordinary Differential Equation (ODE). The proposed architecture performs scene graph inference by solving a neural variant of an ODE by end-to-end learning. The connection between (Mixed-)Integer Linear Program and ODEs in combination with the end-to-end training amounts to learning how to solve assignment problems with image-specific objective functions. Intuitive, visual explanations are provided for the role of the single free variable of the ODE modules which are associated with time in many natural processes. The proposed model achieves results equal to or above state-of-the-art on all three benchmark tasks: scene graph generation (SGGEN), classification (SGCLS) and visual relationship detection (PREDCLS) on Visual Genome benchmark. The strong results on scene graph classification support the claim that assignment problems can indeed be solved by neural ODEs.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings
EditorsAndrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm
PublisherSpringer Science and Business Media Deutschland GmbH
Pages636-653
Number of pages18
ISBN (Print)9783030585648
DOIs
Publication statusPublished - 12 Nov 2020
Event16th European Conference on Computer Vision, ECCV 2020 - Glasgow, UK United Kingdom
Duration: 23 Aug 202028 Aug 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12365 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th European Conference on Computer Vision, ECCV 2020
Country/TerritoryUK United Kingdom
CityGlasgow
Period23/08/2028/08/20

Keywords

  • Scene graph
  • Semantic image understanding
  • Visual relationship detection

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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