Multimodal Quasi-AutoRegression: Forecasting the visual popularity of new fashion products

• URL: http://arxiv.org/abs/2204.04014v1
• Date: Fri, 8 Apr 2022 11:53:54 GMT
• Title: Multimodal Quasi-AutoRegression: Forecasting the visual popularity of new fashion products
• Authors: Stefanos I. Papadopoulos, Christos Koutlis, Symeon Papadopoulos, Ioannis Kompatsiaris
• Abstract summary: Trend detection in fashion is a challenging task due to the fast pace of change in the fashion industry. We propose MuQAR, a multi-modal multi-layer perceptron processing categorical and visual features extracted by computer vision networks. A comparative study on the VISUELLE dataset, shows that MuQAR is capable of competing and surpassing the domain's current state of the art by 2.88% in terms of WAPE and 3.04% in terms of MAE.
• Score: 18.753508811614644
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Estimating the preferences of consumers is of utmost importance for the fashion industry as appropriately leveraging this information can be beneficial in terms of profit. Trend detection in fashion is a challenging task due to the fast pace of change in the fashion industry. Moreover, forecasting the visual popularity of new garment designs is even more demanding due to lack of historical data. To this end, we propose MuQAR, a Multimodal Quasi-AutoRegressive deep learning architecture that combines two modules: (1) a multi-modal multi-layer perceptron processing categorical and visual features extracted by computer vision networks and (2) a quasi-autoregressive neural network modelling the time series of the product's attributes, which are used as a proxy of temporal popularity patterns mitigating the lack of historical data. We perform an extensive ablation analysis on two large scale image fashion datasets, Mallzee-popularity and SHIFT15m to assess the adequacy of MuQAR and also use the Amazon Reviews: Home and Kitchen dataset to assess generalisability to other domains. A comparative study on the VISUELLE dataset, shows that MuQAR is capable of competing and surpassing the domain's current state of the art by 2.88% in terms of WAPE and 3.04% in terms of MAE.

Related papers

• MMBench: Is Your Multi-modal Model an All-around Player? [114.45702807380415]
  How to evaluate large vision-language models remains a major obstacle, hindering future model development. Traditional benchmarks provide quantitative performance measurements but suffer from a lack of fine-grained ability assessment and non-robust evaluation metrics. Recent subjective benchmarks, such as OwlEval, offer comprehensive evaluations of a model's abilities by incorporating human labor, but they are not scalable and display significant bias. MMBench is a systematically-designed objective benchmark for robustly evaluating the various abilities of vision-language models.
  arXiv  Detail & Related papers  (2023-07-12T16:23:09Z)
• Data Efficient Training with Imbalanced Label Sample Distribution for Fashion Detection [5.912870746288055]
  We propose a state-of-the-art weighted objective function to boost the performance of deep neural networks (DNNs) for multi-label classification with long-tailed data distribution. Our experiments involve image-based attribute classification of fashion apparels, and the results demonstrate favorable performance for the new weighting method.
  arXiv  Detail & Related papers  (2023-05-07T21:25:09Z)
• Design-time Fashion Popularity Forecasting in VR Environments [9.621518697689128]
  We develop a computer vision pipeline fine tuned on fashion imagery to extract relevant visual features. We propose MuQAR, a Multimodal Quasi-AutoRegressive neural network that forecasts the popularity of new garments. Both the proposed HLS and MuQAR prove capable of surpassing the current state-of-the-art in key benchmark datasets.
  arXiv  Detail & Related papers  (2022-12-14T12:30:03Z)
• Multimodal Neural Network For Demand Forecasting [0.8602553195689513]
  We propose a multi-modal sales forecasting network that combines real-life events from news articles with traditional data such as historical sales and holiday information. We show statistically significant improvements in the SMAPE error metric with an average improvement of 7.37% against the existing state-of-the-art sales forecasting techniques.
  arXiv  Detail & Related papers  (2022-10-20T18:06:36Z)
• 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)
• Perceptual Score: What Data Modalities Does Your Model Perceive? [73.75255606437808]
  We introduce the perceptual score, a metric that assesses the degree to which a model relies on the different subsets of the input features. We find that recent, more accurate multi-modal models for visual question-answering tend to perceive the visual data less than their predecessors. Using the perceptual score also helps to analyze model biases by decomposing the score into data subset contributions.
  arXiv  Detail & Related papers  (2021-10-27T12:19:56Z)
• Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google Trends [13.873453929997776]
  New fashion product sales forecasting is a challenging problem that involves many business dynamics. We propose a neural network-based approach, where an encoder learns a representation of the time series. Our model works in a non-autoregressive manner, avoiding the compounding effect of large first-step errors.
  arXiv  Detail & Related papers  (2021-09-20T20:15:08Z)
• Leveraging Multiple Relations for Fashion Trend Forecasting Based on Social Media [72.06420633156479]
  We propose an improved model named Relation Enhanced Attention Recurrent (REAR) network. Compared to KERN, the REAR model leverages not only the relations among fashion elements but also those among user groups. To further improve the performance of long-range trend forecasting, the REAR method devises a sliding temporal attention mechanism.
  arXiv  Detail & Related papers  (2021-05-07T14:52:03Z)
• 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)
• 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.