Performance-efficiency Trade-off For Fashion Image Retrieval

Abstract

The fashion industry has been identified as a major contributor to waste and emissions, leading to an increased interest in promoting the second-hand market. Machine learning methods play an important role in facilitating the creation and expansion of second-hand marketplaces by enabling the large-scale valuation of used garments. We contribute to this line of work by addressing the scalability of second-hand image retrieval from databases. By introducing a selective representation framework, we can shrink databases to 10% of their original size without sacrificing retrieval accuracy. We first explore clustering and coreset selection methods to identify representative samples that capture the key features of each garment and its internal variability. Then, we introduce an efficient outlier removal method, based on a neighbour-homogeneity consistency score measure, that filters out uncharacteristic samples prior to selection. We evaluate our approach on three public datasets: DeepFashio

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyhurtado2025performance

Related papers

• A Strong Baseline For Fashion Retrieval With Person Re-identification Models (2020)8.09
• Fashion-rag: Multimodal Fashion Image Editing Via Retrieval-augmented Generation (2025)4.52
• An Effective Pipeline For A Real-world Clothes Retrieval System (2020)0.00
• Lookbench: A Live And Holistic Open Benchmark For Fashion Image Retrieval (2026)0.00
• Leveraging Weakly Annotated Data For Fashion Image Retrieval And Label Prediction (2017)14.06
• Snap And Find: Deep Discrete Cross-domain Garment Image Retrieval (2019)0.00
• Kisekloset For Fashion Retrieval And Recommendation (2025)0.00
• Snapmode: An Intelligent And Distributed Large-scale Fashion Image Retrieval Platform Based On Big Data And Deep Generative Adversarial Network Technologies (2022)0.00