activity
20232026
most citedA Machine Learning-oriented Survey on Tiny Machine Learning

4 citations · 5 across the 12 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.LG2024

MDiFF: Exploiting Multimodal Score-based Diffusion Models for New Fashion Product Performance Forecasting

Andrea Avogaro, Luigi Capogrosso, Franco Fummi +1

The fast fashion industry suffers from significant environmental impacts due to overproduction and unsold inventory. Accurately predicting sales volumes for unreleased products cou…

cs.CV2024

Dif4FF: Leveraging Multimodal Diffusion Models and Graph Neural Networks for Accurate New Fashion Product Performance Forecasting

Andrea Avogaro, Luigi Capogrosso, Franco Fummi +1

In the fast-fashion industry, overproduction and unsold inventory create significant environmental problems. Precise sales forecasts for unreleased items could drastically improve…

cs.CV2024

SITUATE: Indoor Human Trajectory Prediction through Geometric Features and Self-Supervised Vision Representation

Luigi Capogrosso, Andrea Toaiari, Andrea Avogaro +4

Patterns of human motion in outdoor and indoor environments are substantially different due to the scope of the environment and the typical intentions of people therein. While outd…

cs.LG2024

Enhancing Split Computing and Early Exit Applications through Predefined Sparsity

Luigi Capogrosso, Enrico Fraccaroli, Giulio Petrozziello +4

In the past decade, Deep Neural Networks (DNNs) achieved state-of-the-art performance in a broad range of problems, spanning from object classification and action recognition to sm…

cs.CV2024★ 1 cited

Leveraging Latent Diffusion Models for Training-Free In-Distribution Data Augmentation for Surface Defect Detection

Federico Girella, Ziyue Liu, Franco Fummi +3

Defect detection is the task of identifying defects in production samples. Usually, defect detection classifiers are trained on ground-truth data formed by normal samples (negative…

cs.LG2024

MTL-Split: Multi-Task Learning for Edge Devices using Split Computing

Luigi Capogrosso, Enrico Fraccaroli, Samarjit Chakraborty +2

Split Computing (SC), where a Deep Neural Network (DNN) is intelligently split with a part of it deployed on an edge device and the rest on a remote server is emerging as a promisi…