Panoptic Scene Graph Generation
9 papers with code • 1 benchmarks • 1 datasets
PSG task abstracts the given image with a scene graph, where nodes are grounded by panoptic segmentation
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We then introduce Stacked Motif Networks, a new architecture designed to capture higher order motifs in scene graphs that further improves over our strong baseline by an average 7. 1% relative gain.
We propose to compose dynamic tree structures that place the objects in an image into a visual context, helping visual reasoning tasks such as scene graph generation and visual Q&A.
In this work, we explicitly model the objects and their relationships using scene graphs, a visually-grounded graphical structure of an image.
Existing unbiased methods tackle the long-tail problem by data/loss rebalancing to favor low-frequency relations.
Panoptic Scene Graph (PSG) is a challenging task in Scene Graph Generation (SGG) that aims to create a more comprehensive scene graph representation using panoptic segmentation instead of boxes.
To promise consistency and accuracy during the transfer process, we propose to measure the invariance of representations in each predicate class, and learn unbiased prototypes of predicates with different intensities.
Panoptic Scene Graph Generation (PSG) aims at achieving a comprehensive image understanding by simultaneously segmenting objects and predicting relations among objects.