Learning Tuple Compatibility for Conditional OutfitRecommendation

• URL: http://arxiv.org/abs/2008.08189v1
• Date: Tue, 18 Aug 2020 23:22:16 GMT
• Title: Learning Tuple Compatibility for Conditional OutfitRecommendation
• Authors: Xuewen Yang, Dongliang Xie, Xin Wang, Jiangbo Yuan, Wanying Ding, Pengyun Yan
• Abstract summary: Mixed Category Attention Net (MCAN) integrates fine-grained and coarse category information into recommendation. MCAN can explicitly and effectively generate diverse and controllable recommendations based on need. New dataset IQON can be used to test the generalization of recommendation systems.
• Score: 13.265372545945112
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Outfit recommendation requires the answers of some challenging outfit compatibility questions such as 'Which pair of boots and school bag go well with my jeans and sweater?'. It is more complicated than conventional similarity search, and needs to consider not only visual aesthetics but also the intrinsic fine-grained and multi-category nature of fashion items. Some existing approaches solve the problem through sequential models or learning pair-wise distances between items. However, most of them only consider coarse category information in defining fashion compatibility while neglecting the fine-grained category information often desired in practical applications. To better define the fashion compatibility and more flexibly meet different needs, we propose a novel problem of learning compatibility among multiple tuples (each consisting of an item and category pair), and recommending fashion items following the category choices from customers. Our contributions include: 1) Designing a Mixed Category Attention Net (MCAN) which integrates both fine-grained and coarse category information into recommendation and learns the compatibility among fashion tuples. MCAN can explicitly and effectively generate diverse and controllable recommendations based on need. 2) Contributing a new dataset IQON, which follows eastern culture and can be used to test the generalization of recommendation systems. Our extensive experiments on a reference dataset Polyvore and our dataset IQON demonstrate that our method significantly outperforms state-of-the-art recommendation methods.

Related papers

• Preview-based Category Contrastive Learning for Knowledge Distillation [53.551002781828146]
  We propose a novel preview-based category contrastive learning method for knowledge distillation (PCKD) It first distills the structural knowledge of both instance-level feature correspondence and the relation between instance features and category centers. It can explicitly optimize the category representation and explore the distinct correlation between representations of instances and categories.
  arXiv  Detail & Related papers  (2024-10-18T03:31:00Z)
• MMTryon: Multi-Modal Multi-Reference Control for High-Quality Fashion Generation [70.83668869857665]
  MMTryon is a multi-modal multi-reference VIrtual Try-ON framework. It can generate high-quality compositional try-on results by taking a text instruction and multiple garment images as inputs.
  arXiv  Detail & Related papers  (2024-05-01T11:04:22Z)
• 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)
• Outfit Generation and Recommendation -- An Experimental Study [0.0]
  We compare different algorithms for outfit generation and recommendation using online, real-world user data from one of Europe's largest fashion stores. We present the adaptations we made to some of those models to make them suitable for personalized outfit generation.
  arXiv  Detail & Related papers  (2022-11-29T16:36:00Z)
• VICTOR: Visual Incompatibility Detection with Transformers and Fashion-specific contrastive pre-training [18.753508811614644]
  Visual InCompatibility TransfORmer (VICTOR) is optimized for two tasks: 1) overall compatibility as regression and 2) the detection of mismatching items. We build upon the Polyvore outfit benchmark to generate partially mismatching outfits, creating a new dataset termed Polyvore-MISFITs. A series of ablation and comparative analyses show that the proposed architecture can compete and even surpass the current state-of-the-art on Polyvore datasets.
  arXiv  Detail & Related papers  (2022-07-27T11:18:55Z)
• Hybrid Relation Guided Set Matching for Few-shot Action Recognition [51.3308583226322]
  We propose a novel Hybrid Relation guided Set Matching (HyRSM) approach that incorporates two key components. The purpose of the hybrid relation module is to learn task-specific embeddings by fully exploiting associated relations within and cross videos in an episode. We evaluate HyRSM on six challenging benchmarks, and the experimental results show its superiority over the state-of-the-art methods by a convincing margin.
  arXiv  Detail & Related papers  (2022-04-28T11:43:41Z)
• Fashionformer: A simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition [80.74495836502919]
  In this work, we focus on joint human fashion segmentation and attribute recognition. We introduce the object query for segmentation and the attribute query for attribute prediction. For attribute stream, we design a novel Multi-Layer Rendering module to explore more fine-grained features.
  arXiv  Detail & Related papers  (2022-04-10T11:11:10Z)
• Discovering Relationships between Object Categories via Universal Canonical Maps [80.07703460198198]
  We tackle the problem of learning the geometry of multiple categories of deformable objects jointly. Recent work has shown that it is possible to learn a unified dense pose predictor for several categories of related objects. We show that improved correspondences can be learned automatically as a natural byproduct of learning category-specific dense pose predictors.
  arXiv  Detail & Related papers  (2021-06-17T18:38:18Z)
• 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.