6 papers
Erased but Exploitable: Black-box Embedding-Aware Prompting Against Unlearned Text-to-Image Diffusion Models
Arian Komaei Koma, Seyed Amir Kasaei, AmirMahdi Sadeghzadeh +1
Machine unlearning aims to remove specific concepts from pretrained text-to-image diffusion models, yet several white- and black-box attacks have been introduced to make the model…
CARINOX: Inference-time Scaling with Category-Aware Reward-based Initial Noise Optimization and Exploration
Seyed Amir Kasaei, Ali Aghayari, Arash Marioriyad +5
Text-to-image diffusion models, such as Stable Diffusion, can produce high-quality and diverse images but often fail to achieve compositional alignment, particularly when prompts d…
Erasure or Erosion? Evaluating Compositional Degradation in Unlearned Text-To-Image Diffusion Models
Arian Komaei Koma, Seyed Amir Kasaei, Ali Aghayari +2
Post-hoc unlearning has emerged as a practical mechanism for removing undesirable concepts from large text-to-image diffusion models. However, prior work primarily evaluates unlear…
Hidden Meanings in Plain Sight: RebusBench for Evaluating Cognitive Visual Reasoning
Seyed Amir Kasaei, Arash Marioriyad, Mahbod Khaleti +3
Large Vision-Language Models (LVLMs) have achieved remarkable proficiency in explicit visual recognition, effectively describing what is directly visible in an image. However, a cr…
Hallucination as an Upper Bound: A New Perspective on Text-to-Image Evaluation
Seyed Amir Kasaei, Mohammad Hossein Rohban
In language and vision-language models, hallucination is broadly understood as content generated from a model's prior knowledge or biases rather than from the given input. While th…
Evaluating the Evaluators: Metrics for Compositional Text-to-Image Generation
Seyed Amir Kasaei, Ali Aghayari, Arash Marioriyad +4
Text-image generation has advanced rapidly, but assessing whether outputs truly capture the objects, attributes, and relations described in prompts remains a central challenge. Eva…