TY - GEN

T1 - NODIS

T2 - 16th European Conference on Computer Vision<br/>

AU - Yuren, Cong

AU - Ackermann, Hanno

AU - Liao, Wentong

AU - Yang, Michael Ying

AU - Rosenhahn, Bodo

N1 - Funding Information: Acknowledgement. This work was partially supported by the DFG grant COVMAP (RO 2497/12-2) and EXC 2122.

PY - 2020/11/14

Y1 - 2020/11/14

N2 - 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. It achieves state-of-the-art results on all three benchmark tasks: scene graph generation (SGGen), classification (SGCls) and visual relationship detection (PredCls) on Visual Genome benchmark.

AB - 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. It achieves state-of-the-art results on all three benchmark tasks: scene graph generation (SGGen), classification (SGCls) and visual relationship detection (PredCls) on Visual Genome benchmark.

KW - cs.CV

KW - Visual relationship detection

KW - Scene graph

KW - Semantic image understanding

UR - http://www.scopus.com/inward/record.url?scp=85097433441&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-58565-5_38

DO - 10.1007/978-3-030-58565-5_38

M3 - Conference contribution

SN - 978-3-030-58564-8

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 636

EP - 653

BT - Computer Vision – ECCV 2020

A2 - Vedaldi, Andrea

A2 - Bischof, Horst

A2 - Brox, Thomas

A2 - Frahm, Jan-Michael

Y2 - 23 August 2020 through 28 August 2020

ER -