Deep learning techniques have led to remarkable breakthroughs in the field of generic
object detection and have spawned a lot of scene-understanding tasks in recent years …

Deep learning techniques have led to remarkable breakthroughs in the field of object
detection and have spawned a lot of scene-understanding tasks in recent years. Scene …

The heterogeneity in recently published knowledge graph embedding models'
implementations, training, and evaluation has made fair and thorough comparisons difficult …

A comprehensive representation of an image requires understanding objects and their
mutual relationship, especially in image-to-graph generation, eg, road network extraction …

Recent years have seen a growing interest in Scene Graph Generation (SGG), a
comprehensive visual scene understanding task that aims to predict entity relationships …

A major challenge in scene graph classification is that the appearance of objects and
relations can be significantly different from one image to another. Previous works have …

Abstract Panoptic Scene Graph generation (PSG) is a recently proposed task in image
scene understanding that aims to segment the image and extract triplets of subjects, objects …

The link prediction task on knowledge graphs without explicit negative triples in the training
data motivates the usage of rank-based metrics. Here, we review existing rank-based …

Training scene graph classification models requires a large amount of annotated image
data. Meanwhile, scene graphs represent relational knowledge that can be modeled with …

The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed
proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel …