Sina Malakouti

Sina Malakouti

he/him

CS PhD Candidate @ UPitt

Publications

For full list, see Google Scholar.

Culture in Action: Evaluating Text-to-Image Models through Social Activities

Published in The Fourteenth International Conference on Learning Representations (ICLR), 2026

This paper introduces CULTIVate, a benchmark for evaluating cultural faithfulness in text-to-image models using cross-cultural everyday activities. It proposes descriptor-based metrics to measure cultural alignment, hallucination, and exaggeration, and shows that current models perform unevenly across regions, with stronger results for Global North cultures.

Culture in Action: Evaluating Text-to-Image Models through Social Activities

Role Bias in Diffusion Models: Diagnosing and Mitigating through Intermediate Decomposition

Published in The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS), 2025

This work introduces RoleBench, a benchmark for evaluating compositional generalization in text-to-image models through action-based relations. It identifies a systematic failure—RoleCollapse—where models default to frequent reversed relations, and shows that a lightweight intermediate fine-tuning approach (ReBind) can significantly reduce role bias and improve compositional generation.

Role Bias in Diffusion Models: Diagnosing and Mitigating through Intermediate Decomposition

Benchmarking VLMs’ Reasoning About Persuasive Atypical Images

Published in IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025

We study how vision–language models interpret persuasive advertisements with atypical portrayal of objects. We introduce 3 new tasks to evaluate the visual reasoning abilities of VLMs and MLLMs in understanding atypical imagery. We further compare the visual reasoning capabilities of VLMs and LLMs via purposing an atypicality-aware chain-of-thought prompting method. Our findings show that current VLMs and MLLMs struggle with reasoning over atypical images in creative ads and tend to rely on shallow visual cues (e.g., object recognition), leading to significantly more semantic errors than LLMs when faced with semantically challenging negatives.

Benchmarking VLMs’ Reasoning About Persuasive Atypical Images