Relationformer: A Unified Framework for Image-to-Graph Generation
- URL: http://arxiv.org/abs/2203.10202v1
- Date: Sat, 19 Mar 2022 00:36:59 GMT
- Title: Relationformer: A Unified Framework for Image-to-Graph Generation
- Authors: Suprosanna Shit, Rajat Koner, Bastian Wittmann, Johannes Paetzold,
Ivan Ezhov, Hongwei Li, Jiazhen Pan, Sahand Sharifzadeh, Georgios Kaissis,
Volker Tresp, Bjoern Menze
- Abstract summary: This work proposes a unified one-stage transformer-based framework, namely Relationformer, that jointly predicts objects and their relations.
We leverage direct set-based object prediction and incorporate the interaction among the objects to learn an object-relation representation jointly.
We achieve state-of-the-art performance on multiple, diverse and multi-domain datasets.
- Score: 18.832626244362075
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: A comprehensive representation of an image requires understanding objects and
their mutual relationship, especially in image-to-graph generation, e.g., road
network extraction, blood-vessel network extraction, or scene graph generation.
Traditionally, image-to-graph generation is addressed with a two-stage approach
consisting of object detection followed by a separate relation prediction,
which prevents simultaneous object-relation interaction. This work proposes a
unified one-stage transformer-based framework, namely Relationformer, that
jointly predicts objects and their relations. We leverage direct set-based
object prediction and incorporate the interaction among the objects to learn an
object-relation representation jointly. In addition to existing [obj]-tokens,
we propose a novel learnable token, namely [rln]-token. Together with
[obj]-tokens, [rln]-token exploits local and global semantic reasoning in an
image through a series of mutual associations. In combination with the
pair-wise [obj]-token, the [rln]-token contributes to a computationally
efficient relation prediction. We achieve state-of-the-art performance on
multiple, diverse and multi-domain datasets that demonstrate our approach's
effectiveness and generalizability.
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