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Found 10 papers, 1 papers with code
Source-Free Online Domain Adaptive Semantic Segmentation of Satellite Images under Image Degradation
In this paper, we address source-free and online domain adaptation, i. e., test-time adaptation (TTA), for satellite images, with the focus on mitigating distribution shifts caused by various forms of image degradation.
MeTA: Multi-source Test Time Adaptation
Test time adaptation is the process of adapting, in an unsupervised manner, a pre-trained source model to each incoming batch of the test data (i. e., without requiring a substantial portion of the test data to be available, as in traditional domain adaptation) and without access to the source data.
Active Learning Guided Federated Online Adaptation: Applications in Medical Image Segmentation
Motivated by these, we propose a method for medical image segmentation that adapts to each incoming data batch (online adaptation), incorporates physician feedback through active learning, and assimilates knowledge across facilities in a federated setup.
Effective Restoration of Source Knowledge in Continual Test Time Adaptation
By restoring the knowledge from the source, it effectively corrects the negative consequences arising from the gradual deterioration of model parameters caused by ongoing shifts in the domain.
HRFNet: High-Resolution Forgery Network for Localizing Satellite Image Manipulation
Existing high-resolution satellite image forgery localization methods rely on patch-based or downsampling-based training.
STLGRU: Spatio-Temporal Lightweight Graph GRU for Traffic Flow Prediction
Specifically, our proposed STLGRU can effectively capture dynamic local and global spatial-temporal relations of traffic networks using memory-augmented attention and gating mechanisms in a continuously synchronized manner.
CFL-Net: Image Forgery Localization Using Contrastive Learning
A key assumption in underlying forged region localization is that there remains a difference of feature distribution between untampered and manipulated regions in each forged image sample, irrespective of the forgery type.
Deep Dive into Semi-Supervised ELBO for Improving Classification Performance
Experiments on a diverse datasets verify that our method can be used to improve the classification performance of existing VAE based semi-supervised models.
A Novel Disaster Image Dataset and Characteristics Analysis using Attention Model
In this research, we have carefully accumulated a relatively challenging dataset that contains images collected from various sources for three different disasters: fire, water and land.
Attention Toward Neighbors: A Context Aware Framework for High Resolution Image Segmentation
High-resolution image segmentation remains challenging and error-prone due to the enormous size of intermediate feature maps.
