48 citations · 139 across the 37 of their papers we have counts for
28 papers · 1 filter
Mise-en-Scène: Implicit Layout Emergence in Diffusion Transformers for Human-AI Design Co-Creation
Zipeng Xu, Ryan Murdock, Umberto Michieli
Automating graphic design synthesis from user-provided elements requires both a coherent overall composition and the exact preservation of each asset. Existing methods predict a la…
Feature-Space Generative Models for One-Shot Class-Incremental Learning
Jack Foster, Kirill Paramonov, Mete Ozay +1
Few-shot class-incremental learning (FSCIL) is a paradigm where a model, initially trained on a dataset of base classes, must adapt to an expanding problem space by recognizing nov…
FedPromo: Federated Lightweight Proxy Models at the Edge Bring New Domains to Foundation Models
Matteo Caligiuri, Francesco Barbato, Donald Shenaj +2
Federated Learning (FL) is an established paradigm for training deep learning models on decentralized data. However, as the size of the models grows, conventional FL approaches oft…
Controllable Forgetting Mechanism for Few-Shot Class-Incremental Learning
Kirill Paramonov, Mete Ozay, Eunju Yang +2
Class-incremental learning in the context of limited personal labeled samples (few-shot) is critical for numerous real-world applications, such as smart home devices. A key challen…
LoRA.rar: Learning to Merge LoRAs via Hypernetworks for Subject-Style Conditioned Image Generation
Donald Shenaj, Ondrej Bohdal, Mete Ozay +2
Recent advancements in image generation models have enabled personalized image creation with both user-defined subjects (content) and styles. Prior works achieved personalization b…
DreamCache: Finetuning-Free Lightweight Personalized Image Generation via Feature Caching
Emanuele Aiello, Umberto Michieli, Diego Valsesia +2
Personalized image generation requires text-to-image generative models that capture the core features of a reference subject to allow for controlled generation across different con…
Swiss DINO: Efficient and Versatile Vision Framework for On-device Personal Object Search
Kirill Paramonov, Jia-Xing Zhong, Umberto Michieli +2
In this paper, we address a recent trend in robotic home appliances to include vision systems on personal devices, capable of personalizing the appliances on the fly. In particular…
Cross-Architecture Auxiliary Feature Space Translation for Efficient Few-Shot Personalized Object Detection
Francesco Barbato, Umberto Michieli, Jijoong Moon +2
Recent years have seen object detection robotic systems deployed in several personal devices (e.g., home robots and appliances). This has highlighted a challenge in their design, i…
Object-conditioned Bag of Instances for Few-Shot Personalized Instance Recognition
Umberto Michieli, Jijoong Moon, Daehyun Kim +1
Nowadays, users demand for increased personalization of vision systems to localize and identify personal instances of objects (e.g., my dog rather than dog) from a few-shot dataset…
FFT-based Selection and Optimization of Statistics for Robust Recognition of Severely Corrupted Images
Elena Camuffo, Umberto Michieli, Jijoong Moon +2
Improving model robustness in case of corrupted images is among the key challenges to enable robust vision systems on smart devices, such as robotic agents. Particularly, robust te…
A Modular System for Enhanced Robustness of Multimedia Understanding Networks via Deep Parametric Estimation
Francesco Barbato, Umberto Michieli, Mehmet Kerim Yucel +2
In multimedia understanding tasks, corrupted samples pose a critical challenge, because when fed to machine learning models they lead to performance degradation. In the past, three…
RECALL+: Adversarial Web-based Replay for Continual Learning in Semantic Segmentation
Chang Liu, Giulia Rizzoli, Francesco Barbato +5
Catastrophic forgetting of previous knowledge is a critical issue in continual learning typically handled through various regularization strategies. However, existing methods strug…
Learning with Style: Continual Semantic Segmentation Across Tasks and Domains
Marco Toldo, Umberto Michieli, Pietro Zanuttigh
Deep learning models dealing with image understanding in real-world settings must be able to adapt to a wide variety of tasks across different domains. Domain adaptation and class…
Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning
Donald Shenaj, Eros Fanì, Marco Toldo +6
Federated Learning (FL) has recently emerged as a possible way to tackle the domain shift in real-world Semantic Segmentation (SS) without compromising the private nature of the co…
SELMA: SEmantic Large-scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints
Paolo Testolina, Francesco Barbato, Umberto Michieli +3
Accurate scene understanding from multiple sensors mounted on cars is a key requirement for autonomous driving systems. Nowadays, this task is mainly performed through data-hungry…
Continual Coarse-to-Fine Domain Adaptation in Semantic Segmentation
Donald Shenaj, Francesco Barbato, Umberto Michieli +1
Deep neural networks are typically trained in a single shot for a specific task and data distribution, but in real world settings both the task and the domain of application can ch…
RECALL: Replay-based Continual Learning in Semantic Segmentation
Andrea Maracani, Umberto Michieli, Marco Toldo +1
Deep networks allow to obtain outstanding results in semantic segmentation, however they need to be trained in a single shot with a large amount of data. Continual learning setting…
Road Scenes Segmentation Across Different Domains by Disentangling Latent Representations
Francesco Barbato, Umberto Michieli, Marco Toldo +1
Deep learning models obtain impressive accuracy in road scenes understanding, however they need a large quantity of labeled samples for their training. Additionally, such models do…
Latent Space Regularization for Unsupervised Domain Adaptation in Semantic Segmentation
Francesco Barbato, Marco Toldo, Umberto Michieli +1
Deep convolutional neural networks for semantic segmentation achieve outstanding accuracy, however they also have a couple of major drawbacks: first, they do not generalize well to…
Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations
Umberto Michieli, Pietro Zanuttigh
Deep neural networks suffer from the major limitation of catastrophic forgetting old tasks when learning new ones. In this paper we focus on class incremental continual learning in…
Unsupervised Domain Adaptation in Semantic Segmentation via Orthogonal and Clustered Embeddings
Marco Toldo, Umberto Michieli, Pietro Zanuttigh
Deep learning frameworks allowed for a remarkable advancement in semantic segmentation, but the data hungry nature of convolutional networks has rapidly raised the demand for adapt…
GMNet: Graph Matching Network for Large Scale Part Semantic Segmentation in the Wild
Umberto Michieli, Edoardo Borsato, Luca Rossi +1
The semantic segmentation of parts of objects in the wild is a challenging task in which multiple instances of objects and multiple parts within those objects must be detected in t…
Unsupervised Domain Adaptation in Semantic Segmentation: a Review
Marco Toldo, Andrea Maracani, Umberto Michieli +1
The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation. This task is attract…
Unsupervised Domain Adaptation with Multiple Domain Discriminators and Adaptive Self-Training
Teo Spadotto, Marco Toldo, Umberto Michieli +1
Unsupervised Domain Adaptation (UDA) aims at improving the generalization capability of a model trained on a source domain to perform well on a target domain for which no labeled d…