Papers by Royi Rassin
DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback (2025.naacl-long)
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Jiao Sun, Deqing Fu, Yushi Hu, Su Wang, Royi Rassin, Da-Cheng Juan, Dana Alon, Charles Herrmann, Sjoerd Van Steenkiste, Ranjay Krishna, Cyrus Rashtchian
| Challenge: | Text-to-Image models (T2I) still struggle to produce images that are both aesthetically pleasing and faithful to the user’s input text. |
| Approach: | They propose a training algorithm that trains T2I models to be faithful to the input text. |
| Outcome: | The proposed model improves both the semantic alignment and aesthetic appeal of two diffusion-based T2I models, evidenced by multiple benchmarks (+1.7% on TIFA, +2.9% on DSG1K, +3.4% on VILA aesthetic). |
Conjunct Resolution in the Face of Verbal Omissions (2023.acl-long)
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| Challenge: | Verbal omissions occur when verbs and arguments are omitted from subsequent clauses . state-of-the-art models struggle with this task, but have limited results . |
| Approach: | They propose a conjunct resolution task that uses a split-and-rephrase paradigm to recover verbal omissions . they propose omitted words in bold and omitted words in red . |
| Outcome: | The proposed method performs decently, but leaves ample room for improvement. |
Evaluating D-MERIT of Partial-annotation on Information Retrieval (2024.emnlp-main)
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Royi Rassin, Yaron Fairstein, Oren Kalinsky, Guy Kushilevitz, Nachshon Cohen, Alexander Libov, Yoav Goldberg
| Challenge: | Using partially-annotated datasets for evaluation can lead to false conclusions . a dataset containing only a subset of relevant passages might result in misleading rankings . |
| Approach: | They propose to use a Wikipedia passage retrieval evaluation set to contain all relevant passages for each query. |
| Outcome: | The proposed dataset can be downloaded from https://d-merit.github.io. |
RefVNLI: Towards Scalable Evaluation of Subject-driven Text-to-image Generation (2025.findings-emnlp)
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Aviv Slobodkin, Hagai Taitelbaum, Yonatan Bitton, Brian Gordon, Michal Sokolik, Nitzan Bitton Guetta, Almog Gueta, Royi Rassin, Dani Lischinski, Idan Szpektor
| Challenge: | Existing methods assess only one aspect of the task, misalign with human judgments or rely on costly API-based evaluation. |
| Approach: | RefVNLI evaluates textual alignment and subject preservation in a single run. |
| Outcome: | RefVNLI outperforms or matches existing baselines across multiple benchmarks and subject categories. |