Lost Your Style? Navigating with Semantic-Level Approach for Text-to-Outfit Retrieval

• URL: http://arxiv.org/abs/2311.02122v1
• Date: Fri, 3 Nov 2023 07:23:21 GMT
• Title: Lost Your Style? Navigating with Semantic-Level Approach for Text-to-Outfit Retrieval
• Authors: Junkyu Jang, Eugene Hwang, Sung-Hyuk Park
• Abstract summary: 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.
• Score: 2.07180164747172
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fashion stylists have historically bridged the gap between consumers' desires and perfect outfits, which involve intricate combinations of colors, patterns, and materials. Although recent advancements in fashion recommendation systems have made strides in outfit compatibility prediction and complementary item retrieval, these systems rely heavily on pre-selected customer choices. Therefore, we introduce a groundbreaking approach to fashion recommendations: text-to-outfit retrieval task that generates a complete outfit set based solely on textual descriptions given by users. Our model is devised at three semantic levels-item, style, and outfit-where each level progressively aggregates data to form a coherent outfit recommendation based on textual input. Here, we leverage strategies similar to those in the contrastive language-image pretraining model to address the intricate-style matrix within the outfit sets. Using the Maryland Polyvore and Polyvore Outfit datasets, our approach significantly outperformed state-of-the-art models in text-video retrieval tasks, solidifying its effectiveness in the fashion recommendation domain. This research not only pioneers a new facet of fashion recommendation systems, but also introduces a method that captures the essence of individual style preferences through textual descriptions.

Related papers

• Decoding Style: Efficient Fine-Tuning of LLMs for Image-Guided Outfit Recommendation with Preference [4.667044856219814]
  This paper presents a novel framework that harnesses the expressive power of large language models (LLMs) for personalized outfit recommendations. We bridge the item visual-textual gap in items descriptions by employing image captioning with a Multimodal Large Language Model (MLLM) The framework is evaluated on the Polyvore dataset, demonstrating its effectiveness in two key tasks: fill-in-the-blank, and complementary item retrieval.
  arXiv  Detail & Related papers  (2024-09-18T17:15:06Z)
• 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)
• 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)
• Recommendation of Compatible Outfits Conditioned on Style [22.03522251199042]
  This work aims to generate outfits conditional on styles or themes as one would dress in real life. We use a novel style encoder network that renders outfit styles in a smooth latent space.
  arXiv  Detail & Related papers  (2022-03-30T09:23:32Z)
• Arbitrary Virtual Try-On Network: Characteristics Preservation and Trade-off between Body and Clothing [85.74977256940855]
  We propose an Arbitrary Virtual Try-On Network (AVTON) for all-type clothes. AVTON can synthesize realistic try-on images by preserving and trading off characteristics of the target clothes and the reference person. Our approach can achieve better performance compared with the state-of-the-art virtual try-on methods.
  arXiv  Detail & Related papers  (2021-11-24T08:59:56Z)
• Semi-Supervised Visual Representation Learning for Fashion Compatibility [17.893627646979038]
  We propose a semi-supervised learning approach to create pseudo-positive and pseudo-negative outfits on the fly during training. For each labeled outfit in a training batch, we obtain a pseudo-outfit by matching each item in the labeled outfit with unlabeled items. We conduct extensive experiments on Polyvore, Polyvore-D and our newly created large-scale Fashion Outfits datasets.
  arXiv  Detail & Related papers  (2021-09-16T15:35:38Z)
• Garment Recommendation with Memory Augmented Neural Networks [28.93484698024234]
  We propose a garment recommendation system to pair different clothing items, namely tops and bottoms, exploiting a Memory Augmented Neural Network (MANN) To refine our recommendations, we then include user preferences via Matrix Factorization. We experiment on IQON3000, a dataset collected from an online fashion community, reporting state of the art results.
  arXiv  Detail & Related papers  (2020-12-11T09:13:14Z)
• Addressing the Cold-Start Problem in Outfit Recommendation Using Visual Preference Modelling [51.147871738838305]
  This paper attempts to address the cold-start problem for new users by leveraging a novel visual preference modelling approach. We demonstrate the use of our approach with feature-weighted clustering to personalise occasion-oriented outfit recommendation.
  arXiv  Detail & Related papers  (2020-08-04T10:07:09Z)
• 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)
• Fashion Recommendation and Compatibility Prediction Using Relational Network [18.13692056232815]
  We develop a Relation Network (RN) to develop new compatibility learning models. FashionRN learns the compatibility of an entire outfit, with an arbitrary number of items, in an arbitrary order. We evaluate our model using a large dataset of 49,740 outfits that we collected from Polyvore website.
  arXiv  Detail & Related papers  (2020-05-13T21:00:54Z)
• Learning Diverse Fashion Collocation by Neural Graph Filtering [78.9188246136867]
  We propose a novel fashion collocation framework, Neural Graph Filtering, that models a flexible set of fashion items via a graph neural network. By applying symmetric operations on the edge vectors, this framework allows varying numbers of inputs/outputs and is invariant to their ordering. We evaluate the proposed approach on three popular benchmarks, the Polyvore dataset, the Polyvore-D dataset, and our reorganized Amazon Fashion dataset.
  arXiv  Detail & Related papers  (2020-03-11T16:17:08Z)

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.