92 citations · 95 across the 9 of their papers we have counts for
9 papers
GDB-Reward: From Evaluation Metrics to Training Rewards for Graphic Design
Adrienne Deganutti, Purvanshi Mehta, Simon Hadfield +1
Text-to-image models excel at natural image synthesis but struggle with graphic design, where success depends on satisfying precise constraints on typography, layout, color, and vi…
Evaluating Design Video Generation: Metrics for Compositional Fidelity
Adrienne Deganutti, Dingning Cao, Jaejung Seol +2
Generative video models are increasingly used in design animation tasks, yet no standardized evaluation framework exists for this domain. Unlike natural video generation, design an…
TASTE: A Designer-Annotated Multi-Dimensional Preference Dataset for AI-Generated Graphic Design
Haonan Zhu, Elad Hirsch, Alexandria Minetti +3
Text-to-image models now generate graphic design at production scale, yet their supervision still comes primarily from photo-style preference datasets with a single overall verdict…
Structural Evaluation Metrics for SVG Generation via Leave-One-Out Analysis
Haonan Zhu, Adrienne Deganutti, Elad Hirsch +1
SVG generation is typically evaluated by comparing rendered outputs to reference images, which captures visual similarity but not the structural properties that make SVG editable,…
Graphic-Design-Bench: A Comprehensive Benchmark for Evaluating AI on Graphic Design Tasks
Adrienne Deganutti, Elad Hirsch, Haonan Zhu +2
We introduce GraphicDesignBench (GDB), the first comprehensive benchmark suite designed specifically to evaluate AI models on the full breadth of professional graphic design tasks.…
LICA: Layered Image Composition Annotations for Graphic Design Research
Elad Hirsch, Shubham Yadav, Mohit Garg +1
We introduce LICA (Layered Image Composition Annotations), a large scale dataset of 1,550,244 multi-layer graphic design compositions designed to advance structured understanding a…
Interpretable Tensor Fusion
Saurabh Varshneya, Antoine Ledent, Philipp Liznerski +4
Conventional machine learning methods are predominantly designed to predict outcomes based on a single data type. However, practical applications may encompass data of diverse type…
Heterogeneous Graph Neural Network on Semantic Tree
Mingyu Guan, Jack W. Stokes, Qinlong Luo +4
The recent past has seen an increasing interest in Heterogeneous Graph Neural Networks (HGNNs), since many real-world graphs are heterogeneous in nature, from citation graphs to em…
EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs
Namyong Park, Fuchen Liu, Purvanshi Mehta +3
How can we perform knowledge reasoning over temporal knowledge graphs (TKGs)? TKGs represent facts about entities and their relations, where each fact is associated with a timestam…