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Found 28 papers, 8 papers with code
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.
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.
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.
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.
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 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.
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 #3 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 #7 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?
