OpenFashionCLIP: Vision-and-Language Contrastive Learning with Open-Source Fashion Data
This work addresses scalable machine learning solutions for e-commerce and online shopping by providing a more generalizable approach for fashion-related tasks, though it is incremental as it builds on existing CLIP-based techniques.
The authors tackled the problem of automatic tagging and multimodal retrieval in fashion by proposing OpenFashionCLIP, a vision-and-language contrastive learning method trained on open-source data, which achieved significant out-of-domain generalization and consistent improvements in accuracy and recall over state-of-the-art methods.
The inexorable growth of online shopping and e-commerce demands scalable and robust machine learning-based solutions to accommodate customer requirements. In the context of automatic tagging classification and multimodal retrieval, prior works either defined a low generalizable supervised learning approach or more reusable CLIP-based techniques while, however, training on closed source data. In this work, we propose OpenFashionCLIP, a vision-and-language contrastive learning method that only adopts open-source fashion data stemming from diverse domains, and characterized by varying degrees of specificity. Our approach is extensively validated across several tasks and benchmarks, and experimental results highlight a significant out-of-domain generalization capability and consistent improvements over state-of-the-art methods both in terms of accuracy and recall. Source code and trained models are publicly available at: https://github.com/aimagelab/open-fashion-clip.