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cs.CV2026

TASTE: A Designer-Annotated Multi-Dimensional Preference Dataset for AI-Generated Graphic Design

Haonan Zhu, Elad Hirsch, Alexandria Minetti +2

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…

cs.CV2026

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.…

cs.CV2026

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…

cs.CV2024

Image-aware Evaluation of Generated Medical Reports

Gefen Dawidowicz, Elad Hirsch, Ayellet Tal

The paper proposes a novel evaluation metric for automatic medical report generation from X-ray images, VLScore. It aims to overcome the limitations of existing evaluation methods,…

cs.CV2024

MedRAT: Unpaired Medical Report Generation via Auxiliary Tasks

Elad Hirsch, Gefen Dawidowicz, Ayellet Tal

Medical report generation from X-ray images is a challenging task, particularly in an unpaired setting where paired image-report data is unavailable for training. To address this c…

cs.CV2024

MedCycle: Unpaired Medical Report Generation via Cycle-Consistency

Elad Hirsch, Gefen Dawidowicz, Ayellet Tal

Generating medical reports for X-ray images presents a significant challenge, particularly in unpaired scenarios where access to paired image-report data for training is unavailabl…