Can AI decrypt fashion jargon for you?
- URL: http://arxiv.org/abs/2003.08052v1
- Date: Wed, 18 Mar 2020 05:32:04 GMT
- Title: Can AI decrypt fashion jargon for you?
- Authors: Yuan Shen, Shanduojiao Jiang, Muhammad Rizky Wellyanto, and Ranjitha
Kumar
- Abstract summary: It is not clear to people how exactly those low level descriptions can contribute to a style or any high level fashion concept.
In this paper, we proposed a data driven solution to address this concept understanding issues by leveraging a large number of existing product data on fashion sites.
We trained a deep learning model that can explicitly predict and explain high level fashion concepts in a product image with its low level and domain specific fashion features.
- Score: 24.45460909986741
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: When people talk about fashion, they care about the underlying meaning of
fashion concepts,e.g., style.For example, people ask questions like what
features make this dress smart.However, the product descriptions in today
fashion websites are full of domain specific and low level words. It is not
clear to people how exactly those low level descriptions can contribute to a
style or any high level fashion concept. In this paper, we proposed a data
driven solution to address this concept understanding issues by leveraging a
large number of existing product data on fashion sites. We first collected and
categorized 1546 fashion keywords into 5 different fashion categories. Then, we
collected a new fashion product dataset with 853,056 products in total.
Finally, we trained a deep learning model that can explicitly predict and
explain high level fashion concepts in a product image with its low level and
domain specific fashion features.
Related papers
- FashionReGen: LLM-Empowered Fashion Report Generation [61.84580616045145]
We propose an intelligent Fashion Analyzing and Reporting system based on advanced Large Language Models (LLMs)
Specifically, it tries to deliver FashionReGen based on effective catwalk analysis, which is equipped with several key procedures.
It also inspires the explorations of more high-level tasks with industrial significance in other domains.
arXiv Detail & Related papers (2024-03-11T12:29:35Z) - Lost Your Style? Navigating with Semantic-Level Approach for
Text-to-Outfit Retrieval [2.07180164747172]
We introduce a groundbreaking approach to fashion recommendations: text-to-outfit retrieval task that generates a complete outfit set based solely on textual descriptions.
Our model is devised at three semantic levels-item, style, and outfit-where each level progressively aggregates data to form a coherent outfit recommendation.
Using the Maryland Polyvore and Polyvore Outfit datasets, our approach significantly outperformed state-of-the-art models in text-video retrieval tasks.
arXiv Detail & Related papers (2023-11-03T07:23:21Z) - Social Media Fashion Knowledge Extraction as Captioning [61.41631195195498]
We study the task of social media fashion knowledge extraction.
We transform the fashion knowledges into a natural language caption with a sentence transformation method.
Our framework then aims to generate the sentence-based fashion knowledge directly from the social media post.
arXiv Detail & Related papers (2023-09-28T09:07:48Z) - Fashionpedia-Ads: Do Your Favorite Advertisements Reveal Your Fashion
Taste? [30.633812626305552]
We study the correlation between advertisements and fashion taste.
We introduce a new dataset, Fashionpedia-Ads, which asks subjects to provide their preferences on both ad (fashion, beauty, car, and dessert) and fashion product (social network and e-commerce style) images.
arXiv Detail & Related papers (2023-05-03T18:00:42Z) - Fashionpedia-Taste: A Dataset towards Explaining Human Fashion Taste [30.633812626305552]
We introduce an interpretability dataset, Fashionpedia-taste, to explain why a subject like or dislike a fashion image.
Subjects are asked to provide their personal attributes and preference on fashion, such as personality and preferred fashion brands.
Our dataset makes it possible for researchers to build computational models to fully understand and interpret human fashion taste.
arXiv Detail & Related papers (2023-05-03T17:54:50Z) - FashionSAP: Symbols and Attributes Prompt for Fine-grained Fashion
Vision-Language Pre-training [12.652002299515864]
We propose a method for fine-grained fashion vision-language pre-training based on fashion Symbols and Attributes Prompt (FashionSAP)
Firstly, we propose the fashion symbols, a novel abstract fashion concept layer, to represent different fashion items.
Secondly, the attributes prompt method is proposed to make the model learn specific attributes of fashion items explicitly.
arXiv Detail & Related papers (2023-04-11T08:20:17Z) - FaD-VLP: Fashion Vision-and-Language Pre-training towards Unified
Retrieval and Captioning [66.38951790650887]
Multimodal tasks in the fashion domain have significant potential for e-commerce.
We propose a novel fashion-specific pre-training framework based on weakly-supervised triplets constructed from fashion image-text pairs.
We show the triplet-based tasks are an effective addition to standard multimodal pre-training tasks.
arXiv Detail & Related papers (2022-10-26T21:01:19Z) - FashionViL: Fashion-Focused Vision-and-Language Representation Learning [129.49630356651454]
We propose a novel fashion-focused Vision-and-Language (V+L) representation learning framework, dubbed as FashionViL.
It contains two novel fashion-specific pre-training tasks designed particularly to exploit two intrinsic attributes with fashion V+L data.
Extensive experiments show that our FashionViL achieves a new state of the art across five downstream tasks.
arXiv Detail & Related papers (2022-07-17T12:06:27Z) - Personalized Fashion Recommendation from Personal Social Media Data: An
Item-to-Set Metric Learning Approach [71.63618051547144]
We study the problem of personalized fashion recommendation from social media data.
We present an item-to-set metric learning framework that learns to compute the similarity between a set of historical fashion items of a user to a new fashion item.
To validate the effectiveness of our approach, we collect a real-world social media dataset.
arXiv Detail & Related papers (2020-05-25T23:24:24Z) - Knowledge Enhanced Neural Fashion Trend Forecasting [81.2083786318119]
This work focuses on investigating fine-grained fashion element trends for specific user groups.
We first contribute a large-scale fashion trend dataset (FIT) collected from Instagram with extracted time series fashion element records and user information.
We propose a Knowledge EnhancedRecurrent Network model (KERN) which takes advantage of the capability of deep recurrent neural networks in modeling time-series data.
arXiv Detail & Related papers (2020-05-07T07:42:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.