activity
20192024
most citedPyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings

89 citations · 126 across the 8 of their papers we have counts for

collaborators

10 papers

cs.CV2024

GenEARL: A Training-Free Generative Framework for Multimodal Event Argument Role Labeling

Hritik Bansal, Po-Nien Kung, P. Jeffrey Brantingham +2

Multimodal event argument role labeling (EARL), a task that assigns a role for each event participant (object) in an image is a complex challenge. It requires reasoning over the en…

cs.LG2023

MemeGraphs: Linking Memes to Knowledge Graphs

Vasiliki Kougia, Simon Fetzel, Thomas Kirchmair +4

Memes are a popular form of communicating trends and ideas in social media and on the internet in general, combining the modalities of images and text. They can express humor and s…

cs.CV2023

Prior-RadGraphFormer: A Prior-Knowledge-Enhanced Transformer for Generating Radiology Graphs from X-Rays

Yiheng Xiong, Jingsong Liu, Kamilia Zaripova +3

The extraction of structured clinical information from free-text radiology reports in the form of radiology graphs has been demonstrated to be a valuable approach for evaluating th…

physics.ins-det2023★ 24 cited

Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning

Baran Hashemi, Nikolai Hartmann, Sahand Sharifzadeh +2

Simulating high-resolution detector responses is a computationally intensive process that has long been challenging in Particle Physics. Despite the ability of generative models to…

cs.CV2022★ 4 cited

Relationformer: A Unified Framework for Image-to-Graph Generation

Suprosanna Shit, Rajat Koner, Bastian Wittmann +8

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

cs.CV2021

Improving Scene Graph Classification by Exploiting Knowledge from Texts

Sahand Sharifzadeh, Sina Moayed Baharlou, Martin Schmitt +2

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