Muhammet Bastan — accepted AI-conference papers

2025

GENIUS: A Generative Framework for Universal Multimodal Search

Sungyeon Kim, Xinliang Zhu, Xiaofan Lin, Muhammet Bastan, Douglas Gray, Suha Kwak

CVPR 2025poster

Generative retrieval is an emerging approach in information retrieval that generates identifiers (IDs) of target data based on a query, providing an efficient alternative to traditional embedding-based retrieval methods. However, existing models are task-specific and fall short of embedding-based re…

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2020

Dynamically Modulated Deep Metric Learning for Visual Search

Dipu Manandhar, Muhammet Bastan, Kim-Hui Yap

ICASSP 2020accepted

This paper proposes dynamically modulated metric learning (DMML) for learning a tiered similarity space to perform visual search. Existing methods often treat the training samples having different degree of information with equal importance which hinders in capturing the underlying granularities in…

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