Search Results for author: Geri Skenderi

Found 12 papers, 7 papers with code

Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning

no code implementations13 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.

Auxiliary Learning Disentanglement +2

Graph-level Representation Learning with Joint-Embedding Predictive Architectures

1 code implementation27 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.

Contrastive Learning Data Augmentation +3

On the use of learning-based forecasting methods for ameliorating fashion business processes: A position paper

no code implementations9 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.

Management Marketing +1

Leveraging commonsense for object localisation in partial scenes

no code implementations1 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.

Object Position

Toward Smart Doors: A Position Paper

no code implementations23 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.

Position

Pose Forecasting in Industrial Human-Robot Collaboration

1 code implementation24 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.

Human Pose Forecasting

POP: Mining POtential Performance of new fashion products via webly cross-modal query expansion

1 code implementation22 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.

New Product Sales Forecasting Time Series +1

Spatial Commonsense Graph for Object Localisation in Partial Scenes

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.

Object Position

MovingFashion: a Benchmark for the Video-to-Shop Challenge

1 code implementation6 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.

Video-to-Shop

Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google Trends

1 code implementation20 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.

New Product Sales Forecasting Time Series +1

Cannot find the paper you are looking for? You can Submit a new open access paper.