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Found 11 papers, 8 papers with code
IDDA: a large-scale multi-domain dataset for autonomous driving
To support work in this direction, this paper contributes a new large scale, synthetic dataset for semantic segmentation with more than 100 different source visual domains.
Reimagine BiSeNet for Real-Time Domain Adaptation in Semantic Segmentation
To the best of our knowledge, we are the first to present a real-time adversarial approach for assessing the domain adaption problem in semantic segmentation.
Pixel-by-Pixel Cross-Domain Alignment for Few-Shot Semantic Segmentation
The pixel-wise adversarial training is assisted by a novel sample selection procedure, that handles the imbalance between source and target data, and a knowledge distillation strategy, that avoids overfitting towards the few target images.
Incremental Learning in Semantic Segmentation from Image Labels
As opposed to existing approaches, that need to generate pseudo-labels offline, we use an auxiliary classifier, trained with image-level labels and regularized by the segmentation model, to obtain pseudo-supervision online and update the model incrementally.
A Contrastive Distillation Approach for Incremental Semantic Segmentation in Aerial Images
Incremental learning represents a crucial task in aerial image processing, especially given the limited availability of large-scale annotated datasets.
Learning Semantics for Visual Place Recognition through Multi-Scale Attention
In this paper we address the task of visual place recognition (VPR), where the goal is to retrieve the correct GPS coordinates of a given query image against a huge geotagged gallery.
FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving
For similar reasons, Federated Learning has been recently introduced as a new machine learning paradigm aiming to learn a global model while preserving privacy and leveraging data on millions of remote devices.
Augmentation Invariance and Adaptive Sampling in Semantic Segmentation of Agricultural Aerial Images
We observe that the existing methods used for this task are designed without considering two characteristics of the aerial data: (i) the top-down perspective implies that the model cannot rely on a fixed semantic structure of the scene, because the same scene may be experienced with different rotations of the sensor; (ii) there can be a strong imbalance in the distribution of semantic classes because the relevant objects of the scene may appear at extremely different scales (e. g., a field of crops and a small vehicle).
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.
Hierarchical Instance Mixing across Domains in Aerial Segmentation
We investigate the task of unsupervised domain adaptation in aerial semantic segmentation and discover that the current state-of-the-art algorithms designed for autonomous driving based on domain mixing do not translate well to the aerial setting.
Cross-Domain Transfer Learning with CoRTe: Consistent and Reliable Transfer from Black-Box to Lightweight Segmentation Model
Many practical applications require training of semantic segmentation models on unlabelled datasets and their execution on low-resource hardware.
