no code implementations • 13 Oct 2023 • Geri Skenderi, Luigi Capogrosso, Andrea Toaiari, Matteo Denitto, Franco Fummi, Simone Melzi, Marco Cristani
In this paper, we propose a novel framework, dubbed Detaux, whereby a weakly supervised disentanglement procedure is used to discover new unrelated classification tasks and the associated labels that can be exploited with the principal task in any Multi-Task Learning (MTL) model.
1 code implementation • 27 Sep 2023 • Geri Skenderi, Hang Li, Jiliang Tang, Marco Cristani
Joint-Embedding Predictive Architectures (JEPAs) have recently emerged as a novel and powerful technique for self-supervised representation learning.
Ranked #11 on Graph Classification on REDDIT-B
no code implementations • 13 Jul 2023 • Luigi Capogrosso, Alessio Mascolini, Federico Girella, Geri Skenderi, Sebastiano Gaiardelli, Nicola Dall'Ora, Francesco Ponzio, Enrico Fraccaroli, Santa Di Cataldo, Sara Vinco, Enrico Macii, Franco Fummi, Marco Cristani
Industry 4. 0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity.
no code implementations • 9 Nov 2022 • Geri Skenderi, Christian Joppi, Matteo Denitto, Marco Cristani
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.
no code implementations • 1 Nov 2022 • Francesco Giuliari, Geri Skenderi, Marco Cristani, Alessio Del Bue, Yiming Wang
With the proposed graph-based scene representation, we estimate the unknown position of the target object using a Graph Neural Network that implements a novel attentional message passing mechanism.
no code implementations • 23 Sep 2022 • Luigi Capogrosso, Geri Skenderi, Federico Girella, Franco Fummi, Marco Cristani
In particular, a smart door system predicts the intention of people near the door based on the social context of the surrounding environment and then makes rational decisions about whether or not to open the door.
1 code implementation • 24 Jul 2022 • Alessio Sampieri, Guido D'Amely, Andrea Avogaro, Federico Cunico, Geri Skenderi, Francesco Setti, Marco Cristani, Fabio Galasso
Pushing back the frontiers of collaborative robots in industrial environments, we propose a new Separable-Sparse Graph Convolutional Network (SeS-GCN) for pose forecasting.
1 code implementation • 22 Jul 2022 • Christian Joppi, Geri Skenderi, Marco Cristani
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
1 code implementation • 14 Apr 2022 • Geri Skenderi, Christian Joppi, Matteo Denitto, Berniero Scarpa, Marco Cristani
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
1 code implementation • CVPR 2022 • Francesco Giuliari, Geri Skenderi, Marco Cristani, Yiming Wang, Alessio Del Bue
The SCG is used to estimate the unknown position of the target object in two steps: first, we feed the SCG into a novel Proximity Prediction Network, a graph neural network that uses attention to perform distance prediction between the node representing the target object and the nodes representing the observed objects in the SCG; second, we propose a Localisation Module based on circular intersection to estimate the object position using all the predicted pairwise distances in order to be independent of any reference system.
1 code implementation • 6 Oct 2021 • Marco Godi, Christian Joppi, Geri Skenderi, Marco Cristani
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
1 code implementation • 20 Sep 2021 • Geri Skenderi, Christian Joppi, Matteo Denitto, Marco Cristani
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