Improving the Annotation of DeepFashion Images for Fine-grained Attribute Recognition
This work addresses data quality issues for researchers in computer vision focusing on clothing recognition, but it is incremental as it modifies an existing dataset rather than introducing new methods.
The authors tackled the problem of unbalanced and repetitive attribute annotations in the DeepFashion dataset for fine-grained attribute recognition by cleaning and merging attributes per category, resulting in improved performance using a bilinear CNN with pairwise ranking loss.
DeepFashion is a widely used clothing dataset with 50 categories and more than overall 200k images where each image is annotated with fine-grained attributes. This dataset is often used for clothes recognition and although it provides comprehensive annotations, the attributes distribution is unbalanced and repetitive specially for training fine-grained attribute recognition models. In this work, we tailored DeepFashion for fine-grained attribute recognition task by focusing on each category separately. After selecting categories with sufficient number of images for training, we remove very scarce attributes and merge the duplicate ones in each category, then we clean the dataset based on the new list of attributes. We use a bilinear convolutional neural network with pairwise ranking loss function for multi-label fine-grained attribute recognition and show that the new annotations improve the results for such a task. The detailed annotations for each of the selected categories are provided for public use.