Search Results for author:
Found 37 papers, 12 papers with code
Brain-inspired sparse training enables Transformers and LLMs to perform as fully connected
The Cannistraci-Hebb training (CHT) is a brain-inspired method for growing connectivity in DST.
Controllable Forgetting Mechanism for Few-Shot Class-Incremental Learning
A key challenge in these scenarios is balancing the trade-off between adapting to new, personalized classes and maintaining the performance of the model on the original, base classes.
class-incremental learning
Few-Shot Class-Incremental Learning
+1
LoRA.rar: Learning to Merge LoRAs via Hypernetworks for Subject-Style Conditioned Image Generation
Recent advancements in image generation models have enabled personalized image creation with both user-defined subjects (content) and styles.
DreamCache: Finetuning-Free Lightweight Personalized Image Generation via Feature Caching
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 contexts.
Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation
One of the most widely used methods is Low-Rank Adaptation (LoRA), with adaptation update expressed as the product of two low-rank matrices.
Swiss DINO: Efficient and Versatile Vision Framework for On-device Personal Object Search
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.
Enhanced Model Robustness to Input Corruptions by Per-corruption Adaptation of Normalization Statistics
In this paper, we introduce Per-corruption Adaptation of Normalization statistics (PAN) to enhance the model robustness of vision systems.
Cross-Architecture Auxiliary Feature Space Translation for Efficient Few-Shot Personalized Object Detection
We introduce a conditional coarse-to-fine few-shot learner to refine the coarse predictions made by an efficient object detector, showing that using an off-the-shelf model leads to poor personalization due to neural collapse.
Model Merging and Safety Alignment: One Bad Model Spoils the Bunch
Our experiments illustrate the effectiveness of integrating alignment-related data during merging, resulting in models that excel in both domain expertise and alignment.
Object-conditioned Bag of Instances for Few-Shot Personalized Instance Recognition
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 only.
FFT-based Selection and Optimization of Statistics for Robust Recognition of Severely Corrupted Images
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.
Deep Neural Network Models Trained With A Fixed Random Classifier Transfer Better Across Domains
Inspired by NC properties, we explore in this paper the transferability of DNN models trained with their last layer weight fixed according to ETF.
HOP to the Next Tasks and Domains for Continual Learning in NLP
Continual Learning (CL) aims to learn a sequence of problems (i. e., tasks and domains) by transferring knowledge acquired on previous problems, whilst avoiding forgetting of past ones.
A Modular System for Enhanced Robustness of Multimedia Understanding Networks via Deep Parametric Estimation
To this end, we design a small, modular, and efficient (just 2GFLOPs to process a Full HD image) system to enhance input data for robust downstream multimedia understanding with minimal computational cost.
RECALL+: Adversarial Web-based Replay for Continual Learning in Semantic Segmentation
Catastrophic forgetting of previous knowledge is a critical issue in continual learning typically handled through various regularization strategies.
Online Continual Learning in Keyword Spotting for Low-Resource Devices via Pooling High-Order Temporal Statistics
Keyword Spotting (KWS) models on embedded devices should adapt fast to new user-defined words without forgetting previous ones.
A Model for Every User and Budget: Label-Free and Personalized Mixed-Precision Quantization
Recent advancement in Automatic Speech Recognition (ASR) has produced large AI models, which become impractical for deployment in mobile devices.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Online Continual Learning for Robust Indoor Object Recognition
Vision systems mounted on home robots need to interact with unseen classes in changing environments.
Learning from Mistakes: Self-Regularizing Hierarchical Representations in Point Cloud Semantic Segmentation
Recent advances in autonomous robotic technologies have highlighted the growing need for precise environmental analysis.
Learning with Style: Continual Semantic Segmentation Across Tasks and Domains
Then, we address the proposed setup by using style transfer techniques to extend knowledge across domains when learning incremental tasks and a robust distillation framework to effectively recollect task knowledge under incremental domain shift.
Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning
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 collected data.
SELMA: SEmantic Large-scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints
Accurate scene understanding from multiple sensors mounted on cars is a key requirement for autonomous driving systems.
Continual Coarse-to-Fine Domain Adaptation in Semantic Segmentation
In this paper we introduce the novel task of coarse-to-fine learning of semantic segmentation architectures in presence of domain shift.
RECALL: Replay-based Continual Learning in Semantic Segmentation
Replay data are then blended with new samples during the incremental steps.
Road Scenes Segmentation Across Different Domains by Disentangling Latent Representations
Deep learning models obtain impressive accuracy in road scenes understanding, however they need a large quantity of labeled samples for their training.
Prototype Guided Federated Learning of Visual Feature Representations
Federated Learning (FL) is a framework which enables distributed model training using a large corpus of decentralized training data.
Latent Space Regularization for Unsupervised Domain Adaptation in Semantic Segmentation
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 distributions slightly different from the one of the training data; second, they require a huge amount of labeled data for their optimization.
Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations
Second, features sparsification allows to make room in the latent space to accommodate novel classes.
Ranked #4 on
Disjoint 15-5
on PASCAL VOC 2012
Unsupervised Domain Adaptation in Semantic Segmentation via Orthogonal and Clustered Embeddings
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 adaptation techniques able to transfer learned knowledge from label-abundant domains to unlabeled ones.
GMNet: Graph Matching Network for Large Scale Part Semantic Segmentation in the Wild
To tackle part-level ambiguity and localization we propose a novel adjacency graph-based module that aims at matching the relative spatial relationships between ground truth and predicted parts.
Ranked #11 on
Semantic Segmentation
on FMB Dataset
Unsupervised Domain Adaptation in Semantic Segmentation: a Review
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.
Unsupervised Domain Adaptation with Multiple Domain Discriminators and Adaptive Self-Training
We introduce a novel UDA framework where a standard supervised loss on labeled synthetic data is supported by an adversarial module and a self-training strategy aiming at aligning the two domain distributions.
Unsupervised Domain Adaptation for Mobile Semantic Segmentation based on Cycle Consistency and Feature Alignment
The supervised training of deep networks for semantic segmentation requires a huge amount of labeled real world data.
Knowledge Distillation for Incremental Learning in Semantic Segmentation
Deep learning architectures have shown remarkable results in scene understanding problems, however they exhibit a critical drop of performances when they are required to learn incrementally new tasks without forgetting old ones.
Adversarial Learning and Self-Teaching Techniques for Domain Adaptation in Semantic Segmentation
A recently proposed workaround is to train deep networks using synthetic data, but the domain shift between real world and synthetic representations limits the performance.
Incremental Learning Techniques for Semantic Segmentation
To tackle this task we propose to distill the knowledge of the previous model to retain the information about previously learned classes, whilst updating the current model to learn the new ones.
Ranked #4 on
Domain 11-1
on Cityscapes
Complex Network Analysis of Men Single ATP Tennis Matches
Who are the most significant players in the history of men tennis?
