Published on Wed Aug 26 2020

Attr2Style: A Transfer Learning Approach for Inferring Fashion Styles via Apparel Attributes

Rajdeep Hazra Banerjee, Abhinav Ravi, Ujjal Kr Dutta

Fashion e-commerce platforms mostly provide details about low-level Attributes of an apparel (eg, neck type, dress length, collar type) We propose a transfer-learning based image captioning model. The model is trained on a source dataset with sufficient attribute-based ground-truth captions.

0
0
0
Abstract

Popular fashion e-commerce platforms mostly provide details about low-level attributes of an apparel (eg, neck type, dress length, collar type) on their product detail pages. However, customers usually prefer to buy apparel based on their style information, or simply put, occasion (eg, party/ sports/ casual wear). Application of a supervised image-captioning model to generate style-based image captions is limited because obtaining ground-truth annotations in the form of style-based captions is difficult. This is because annotating style-based captions requires a certain amount of fashion domain expertise, and also adds to the costs and manual effort. On the contrary, low-level attribute based annotations are much more easily available. To address this issue, we propose a transfer-learning based image captioning model that is trained on a source dataset with sufficient attribute-based ground-truth captions, and used to predict style-based captions on a target dataset. The target dataset has only a limited amount of images with style-based ground-truth captions. The main motivation of our approach comes from the fact that most often there are correlations among the low-level attributes and the higher-level styles for an apparel. We leverage this fact and train our model in an encoder-decoder based framework using attention mechanism. In particular, the encoder of the model is first trained on the source dataset to obtain latent representations capturing the low-level attributes. The trained model is fine-tuned to generate style-based captions for the target dataset. To highlight the effectiveness of our method, we qualitatively and quantitatively demonstrate that the captions generated by our approach are close to the actual style information for the evaluated apparel. A Proof Of Concept for our model is under pilot at Myntra where it is exposed to some internal users for feedback.

Thu Aug 06 2020
Computer Vision
Fashion Captioning: Towards Generating Accurate Descriptions with Semantic Rewards
Generating accurate descriptions for online fashion items is important not only for enhancing customers' shopping experiences, but also for the increase in online sales. The expressions in an enchanting style could better attract customer interest. The goal of this work is to develop a novel learning framework for accurate fashion captioning.
0
0
0
Wed Oct 24 2018
Computer Vision
A Deep-Learning-Based Fashion Attributes Detection Model
Analyzing fashion attributes is essential in the fashion design process. In this project, we propose a data-driven approach for recognizing fashion attributes. A modified version of Faster R-CNN model is trained on images from a large-scale localization dataset with 594 fine-grained attributes.
0
0
0
Tue Dec 04 2018
Computer Vision
Complete the Look: Scene-based Complementary Product Recommendation
Modeling fashion compatibility is challenging due to its complexity and subjectivity. Existing work focuses on predicting compatibility between product images. We propose a new task which seeks to recommend visually compatible products based on scene images.
0
0
0
Sat Apr 17 2021
Computer Vision
Color Variants Identification in Fashion e-commerce via Contrastive Self-Supervised Representation Learning
In this paper, we utilize deep visual Representation Learning to address an important problem in fashion e-commerce: color variants identification. We observed that existing state-of-the-art SSL methods perform poor, for our problem. To address this, we propose a novel SSL based color variants
5
0
2
Sat Aug 01 2020
Computer Vision
Self-supervised Visual Attribute Learning for Fashion Compatibility
Many self-supervised learning (SSL) methods have been successful in learning semantically meaningful visual representations by solving pretext tasks. However, prior work in SSL focuses on tasks like object recognition, which aim to learn object shapes. These SSL methods perform poorly on downstream tasks where these concepts provide critical
4
0
0
Tue Mar 30 2021
Computer Vision
Kaleido-BERT: Vision-Language Pre-training on Fashion Domain
0
0
0