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Found 10 papers, 4 papers with code
FashionReGen: LLM-Empowered Fashion Report Generation
Fashion analysis refers to the process of examining and evaluating trends, styles, and elements within the fashion industry to understand and interpret its current state, generating fashion reports.
Non-Autoregressive Sentence Ordering
Existing sentence ordering approaches generally employ encoder-decoder frameworks with the pointer net to recover the coherence by recurrently predicting each sentence step-by-step.
Solving Math Word Problems with Reexamination
Math word problem (MWP) solving aims to understand the descriptive math problem and calculate the result, for which previous efforts are mostly devoted to upgrade different technical modules.
Computational Technologies for Fashion Recommendation: A Survey
Fashion recommendation is a key research field in computational fashion research and has attracted considerable interest in the computer vision, multimedia, and information retrieval communities in recent years.
Modeling Field-level Factor Interactions for Fashion Recommendation
Personalized fashion recommendation aims to explore patterns from historical interactions between users and fashion items and thereby predict the future ones.
Reproducibility Companion Paper: Knowledge Enhanced Neural Fashion Trend Forecasting
This companion paper supports the replication of the fashion trend forecasting experiments with the KERN (Knowledge Enhanced Recurrent Network) method that we presented in the ICMR 2020.
Leveraging Two Types of Global Graph for Sequential Fashion Recommendation
Sequential fashion recommendation is of great significance in online fashion shopping, which accounts for an increasing portion of either fashion retailing or online e-commerce.
Leveraging Multiple Relations for Fashion Trend Forecasting Based on Social Media
Towards insightful fashion trend forecasting, previous work [1] proposed to analyze more fine-grained fashion elements which can informatively reveal fashion trends.
Knowledge Enhanced Neural Fashion Trend Forecasting
Further-more, to effectively model the time series data of fashion elements with rather complex patterns, we propose a Knowledge EnhancedRecurrent Network model (KERN) which takes advantage of the capability of deep recurrent neural networks in modeling time-series data.
Bilinear Supervised Hashing Based on 2D Image Features
Hashing has been recognized as an efficient representation learning method to effectively handle big data due to its low computational complexity and memory cost.
