Personalized Fashion Recommendation with Image Attributes and Aesthetics Assessment
- URL: http://arxiv.org/abs/2501.03085v1
- Date: Mon, 06 Jan 2025 15:31:10 GMT
- Title: Personalized Fashion Recommendation with Image Attributes and Aesthetics Assessment
- Authors: Chongxian Chen, Fan Mo, Xin Fan, Hayato Yamana,
- Abstract summary: We aim to provide more accurate personalized fashion recommendations by converting available information, especially images, into two graphs attribute.
Compared with previous methods that separate image and text as two components, the proposed method combines image and text information to create a richer attributes graph.
Preliminary experiments on the IQON3000 dataset have shown that the proposed method achieves competitive accuracy compared with baselines.
- Score: 15.423307815155534
- License:
- Abstract: Personalized fashion recommendation is a difficult task because 1) the decisions are highly correlated with users' aesthetic appetite, which previous work frequently overlooks, and 2) many new items are constantly rolling out that cause strict cold-start problems in the popular identity (ID)-based recommendation methods. These new items are critical to recommend because of trend-driven consumerism. In this work, we aim to provide more accurate personalized fashion recommendations and solve the cold-start problem by converting available information, especially images, into two attribute graphs focusing on optimized image utilization and noise-reducing user modeling. Compared with previous methods that separate image and text as two components, the proposed method combines image and text information to create a richer attributes graph. Capitalizing on the advancement of large language and vision models, we experiment with extracting fine-grained attributes efficiently and as desired using two different prompts. Preliminary experiments on the IQON3000 dataset have shown that the proposed method achieves competitive accuracy compared with baselines.
Related papers
- Multi-subject Open-set Personalization in Video Generation [110.02124633005516]
We present Video Alchemist $-$ a video model with built-in multi-subject, open-set personalization capabilities.
Our model is built on a new Diffusion Transformer module that fuses each conditional reference image and its corresponding subject-level text prompt.
Our method significantly outperforms existing personalization methods in both quantitative and qualitative evaluations.
arXiv Detail & Related papers (2025-01-10T18:59:54Z) - Fashionability-Enhancing Outfit Image Editing with Conditional Diffusion Models [8.632093933229678]
This paper presents a novel diffusion model-based approach that generates fashion images with improved fashionability while maintaining control over key attributes.
Key components of our method include: 1) fashionability enhancement, which ensures that the generated images are more fashionable than the input; 2) preservation of body characteristics, encouraging the generated images to maintain the original shape and proportions of the input; and 3) automatic fashion optimization, which does not rely on manual input or external prompts.
arXiv Detail & Related papers (2024-12-24T13:27:25Z) - JeDi: Joint-Image Diffusion Models for Finetuning-Free Personalized Text-to-Image Generation [49.997839600988875]
Existing personalization methods rely on finetuning a text-to-image foundation model on a user's custom dataset.
We propose Joint-Image Diffusion (jedi), an effective technique for learning a finetuning-free personalization model.
Our model achieves state-of-the-art generation quality, both quantitatively and qualitatively, significantly outperforming both the prior finetuning-based and finetuning-free personalization baselines.
arXiv Detail & Related papers (2024-07-08T17:59:02Z) - Text-to-Image Diffusion Models are Great Sketch-Photo Matchmakers [120.49126407479717]
This paper explores text-to-image diffusion models for Zero-Shot Sketch-based Image Retrieval (ZS-SBIR)
We highlight a pivotal discovery: the capacity of text-to-image diffusion models to seamlessly bridge the gap between sketches and photos.
arXiv Detail & Related papers (2024-03-12T00:02:03Z) - AI Recommendation System for Enhanced Customer Experience: A Novel
Image-to-Text Method [2.2975420753582028]
This research describes an innovative end-to-end pipeline that uses artificial intelligence to provide fine-grained visual interpretation for fashion recommendations.
When customers upload images of desired products or outfits, the system automatically generates meaningful descriptions emphasizing stylistic elements.
arXiv Detail & Related papers (2023-11-16T07:15:44Z) - MetaPortrait: Identity-Preserving Talking Head Generation with Fast
Personalized Adaptation [57.060828009199646]
We propose an ID-preserving talking head generation framework.
We claim that dense landmarks are crucial to achieving accurate geometry-aware flow fields.
We adaptively fuse the source identity during synthesis, so that the network better preserves the key characteristics of the image portrait.
arXiv Detail & Related papers (2022-12-15T18:59:33Z) - End-to-End Image-Based Fashion Recommendation [5.210197476419621]
In fashion-based recommendation settings, incorporating the item image features is considered a crucial factor.
We propose a simple yet effective attribute-aware model that incorporates image features for better item representation learning.
Experiments on two image-based real-world recommender systems datasets show that the proposed model significantly outperforms all state-of-the-art image-based models.
arXiv Detail & Related papers (2022-05-05T21:14:42Z) - Attribute-aware Explainable Complementary Clothing Recommendation [37.30129304097086]
This work aims to tackle the explainability challenge in fashion recommendation tasks by proposing a novel Attribute-aware Fashion Recommender (AFRec)
AFRec recommender assesses the outfit compatibility by explicitly leveraging the extracted attribute-level representations from each item's visual feature.
The attributes serve as the bridge between two fashion items, where we quantify the affinity of a pair of items through the learned compatibility between their attributes.
arXiv Detail & Related papers (2021-07-04T14:56:07Z) - 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) - 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.