Fashion Image Retrieval with Occlusion
Sohn, Jimin Jung, Haeji Yan, Zhiwen Masti, Vibha Li, Xiang Raj, Bhiksha
Sohn, Jimin
Jung, Haeji
Yan, Zhiwen
Masti, Vibha
Li, Xiang
Raj, Bhiksha
Supervisor
Department
Natural Language Processing
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
With the growth of online fashion platforms and independent content creators, there is a growing interest in visually searching for similar clothing items as shown online. In real-world settings, clothes are often covered by other objects, making retrieval challenging. To make fashion image retrieval more robust, we explore fashion image retrieval with occlusion. We conducted various experiments on the In-shop Clothes Retrieval dataset, a subset of the DeepFashion benchmark. We constructed variations of the dataset with different occlusion types, including various sizes and locations of MSCOCO objects and object masks to simulate realistic occlusion circumstances. We evaluate the zero-shot and fine-tuned performance of the state-of-the-art models on these datasets and observe performance drop. We observe that fine-tuning models on one occluded dataset makes the model more robust to other occlusion types and reduces performance drop. The dataset used in this paper can be found in https://bit.ly/4749Mbo.
Citation
J. Sohn, H. Jung, Z. Yan, V. Masti, X. Li, and B. Raj, “Fashion Image Retrieval with Occlusion,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 15321 LNCS, pp. 31–46, 2025, doi: 10.1007/978-3-031-78305-0_3.
Source
International Conference on Pattern Recognition
Conference
Keywords
ART model, Content creators, Fine tuning, Occlusion, On-line fashion, Performance, Real world setting, Robust modeling, State of the art
Subjects
Source
Publisher
Springer Nature