Automatic Defect Segmentation By Unsupervised Anomaly Learning

Fashion image captioning aims to automatically generate product descriptions for fashion items. Existing fashion image captioning models predict a fixed caption for a particular fashion item once deployed. Differently, we explore a controllable way of fashion image captioning by taking the semantic attributes as the control signal. We propose a new multi-modal fashion image captioning method that allows the users to specify a few semantic attributes to guide the caption generation to suit unique preferences. To study the problem, we clean, filter, and assemble a new fashion image caption dataset called FACAD170K from the current FACAD dataset to facilitate learning. We investigate the effectiveness of the proposed approach on our assembled FACAD170K dataset. The results demonstrate that our method can outperform existing fashion image captioning models as well as conventional captioning methods.