Aug 8, 2020 · We propose a set of three novel yet simple self-supervision tasks and train them as auxiliary multi-tasks to the main model. While comparing, we ...
Aug 8, 2020 · Traditional scene graph generation methods are trained using cross-entropy losses that treat objects and relationships as independent entities.
Aug 8, 2020 · This work proposes a set of three novel yet simple self-supervision tasks and train them as auxiliary multi-tasks to the main model and ...
Assisting Scene Graph Generation with Self-Supervision. - dblp
dblp.org › rec › corr › abs-2008-03555
Aug 14, 2020 · Bibliographic details on Assisting Scene Graph Generation with Self-Supervision.
However, learning semantic 3D scene graphs in a fully supervised manner is inherently difficult as it requires not only object-level annotations but also rela-.
In this work we introduce a simple-yet-effective self-supervised relational alignment regularization designed to improve the scene graph generation performance.
Missing: Assisting Supervision.
Our approach extracts a scene graph from an input image using a pretrained scene graph generator and employs semantically-preserving augmentation with self- ...
Feb 7, 2023 · This project demonstrated that spatio-temporal scene graph representations of driving scenes can be effective for AV applications, such as ...
Missing: Assisting | Show results with:Assisting
Jan 29, 2024 · Authors: Bicheng Xu; Renjie Liao; Leonid Sigal Description: The goal of scene graph generation is to predict a graph from an input image, ...
Missing: Assisting | Show results with:Assisting
(3) Scene Graph Generation(SGGEN) is the hardest of the three settings. In this mode, the model is given only an image and is required to output bounding boxes, ...