Bài báo “Scalable Fashion Product Retrieval with Multi-Task Fine-Tuned Vision-Language Models and Real-Time Distributed Architecture” của học viên cao học Đinh Quang Hoàng đã được công bố tại Hội nghị Quốc tế MAPR 2025.
Giảng viên hướng dẫn: TS. Mai Tiến Dũng
Tóm tắt:
This paper presents a novel approach to fashion product retrieval using multi-task fine-tuned vision-language models. We explore and enhance two state-of-the-art models, CLIP and BEiT3, for efficient retrieval of fashion products based on both text descriptions and image queries. Our contributions include: (1) a multi-task fine-tuning method that maintains contrastive capabilities while adding specialized fashion classification tasks, (2) a distributed system architecture for real-time product retrieval that combines modern technologies including FAISS, Apache Kafka, and Apache Spark, and (3) comprehensive benchmarking that identifies the trade-offs between model accuracy and computational efficiency. Experimental results show significant improvements in retrieval performance, with fine-tuned models achieving up to 5.3% increase in Mean Average Precision compared to baseline models. Our system demonstrates practical real-time performance while handling large product catalogs, making it suitable for production e-commerce environments.
The 8th International Conference on Multimedia Analysis and Pattern Recognition (MAPR), supported by the Vietnamese Association on Pattern Recognition (VAPR). MAPR 2025 will be held in Nha Trang, Vietnam, on August 14–15, 2025. The aim of this conference is to bring together researchers and practitioners from academia and industry to share their latest research findings, experimental results, and foster potential collaborations in the areas of pattern recognition, multimedia analysis, and related fields. MAPR is indexed in SCOPUS.
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