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Found 16 papers, 3 papers with code
DynAlign: Unsupervised Dynamic Taxonomy Alignment for Cross-Domain Segmentation
Current unsupervised domain adaptation (UDA) methods for semantic segmentation typically assume identical class labels between the source and target domains.
Learning Intra-view and Cross-view Geometric Knowledge for Stereo Matching
Geometric knowledge has been shown to be beneficial for the stereo matching task.
Prompting Diffusion Representations for Cross-Domain Semantic Segmentation
Intrigued by this result, we set out to explore how well diffusion-pretrained representations generalize to new domains, a crucial ability for any representation.
SF-FSDA: Source-Free Few-Shot Domain Adaptive Object Detection with Efficient Labeled Data Factory
Domain adaptive object detection aims to leverage the knowledge learned from a labeled source domain to improve the performance on an unlabeled target domain.
Continuous Pseudo-Label Rectified Domain Adaptive Semantic Segmentation With Implicit Neural Representations
Unsupervised domain adaptation (UDA) for semantic segmentation aims at improving the model performance on the unlabeled target domain by leveraging a labeled source domain.
One-Shot Domain Adaptive and Generalizable Semantic Segmentation with Class-Aware Cross-Domain Transformers
Thus, we aim to relieve this need on a large number of real data, and explore the one-shot unsupervised sim-to-real domain adaptation (OSUDA) and generalization (OSDG) problem, where only one real-world data sample is available.
GGViT:Multistream Vision Transformer Network in Face2Face Facial Reenactment Detection
The compression of videos on social media has destroyed some pixel details that could be used to detect forgeries.
TACS: Taxonomy Adaptive Cross-Domain Semantic Segmentation
In many real-world settings, the target domain task requires a different taxonomy than the one imposed by the source domain.
Self-Supervised Pretraining and Controlled Augmentation Improve Rare Wildlife Recognition in UAV Images
In detail, we examine a combination of recent contrastive learning methodologies like Momentum Contrast (MoCo) and Cross-Level Instance-Group Discrimination (CLD) to condition our model on the aerial images without the requirement for labels.
A Plant Root System Algorithm Based on Swarm Intelligence for One-dimensional Biomedical Signal Feature Engineering
To date, very few biomedical signals have transitioned from research applications to clinical applications.
mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets
One challenge of object recognition is to generalize to new domains, to more classes and/or to new modalities.
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation
Open compound domain adaptation (OCDA) is a domain adaptation setting, where target domain is modeled as a compound of multiple unknown homogeneous domains, which brings the advantage of improved generalization to unseen domains.
Self-Calibration Supported Robust Projective Structure-from-Motion
In this paper, we propose a unified SfM method, in which the matching process is supported by self-calibration constraints.
Analogical Image Translation for Fog Generation
AIT achieves this zero-shot image translation capability by coupling a supervised training scheme in the synthetic domain, a cycle consistency strategy in the real domain, an adversarial training scheme between the two domains, and a novel network design.
MarmoNet: a pipeline for automated projection mapping of the common marmoset brain from whole-brain serial two-photon tomography
Understanding the connectivity in the brain is an important prerequisite for understanding how the brain processes information.
DLOW: Domain Flow for Adaptation and Generalization
In this work, we present a domain flow generation(DLOW) model to bridge two different domains by generating a continuous sequence of intermediate domains flowing from one domain to the other.
