Search Results for author:
Found 8 papers, 5 papers with code
On the use of learning-based forecasting methods for ameliorating fashion business processes: A position paper
The fashion industry is one of the most active and competitive markets in the world, manufacturing millions of products and reaching large audiences every year.
POP: Mining POtential Performance of new fashion products via webly cross-modal query expansion
We propose a data-centric pipeline able to generate exogenous observation data for the New Fashion Product Performance Forecasting (NFPPF) problem, i. e., predicting the performance of a brand-new clothing probe with no available past observations.
Ranked #1 on
New Product Sales Forecasting
on VISUELLE
The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmark
SO-fore assumes that the season has started and a set of new products is on the shelves of the different stores.
Short-observation new product sales forecasting
Time Series Analysis
MovingFashion: a Benchmark for the Video-to-Shop Challenge
Retrieving clothes which are worn in social media videos (Instagram, TikTok) is the latest frontier of e-fashion, referred to as "video-to-shop" in the computer vision literature.
Ranked #1 on
Video-to-Shop
on MovingFashion
Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google Trends
In particular, we propose a neural network-based approach, where an encoder learns a representation of the exogenous time series, while the decoder forecasts the sales based on the Google Trends encoding and the available visual and metadata information.
Ranked #3 on
New Product Sales Forecasting
on VISUELLE
Texel-Att: Representing and Classifying Element-based Textures by Attributes
It first individuates texels, characterizing them with individual attributes; subsequently, texels are grouped and characterized through layout attributes, which give the Texel-Att representation.
Texture Retrieval in the Wild through detection-based attributes
Capturing the essence of a textile image in a robust way is important to retrieve it in a large repository, especially if it has been acquired in the wild (by taking a photo of the textile of interest).
SIMCO: SIMilarity-based object COunting
We present SIMCO, the first agnostic multi-class object counting approach.
