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Found 5 papers, 2 papers with code
Layer-wise Model Merging for Unsupervised Domain Adaptation in Segmentation Tasks
In this paper, we leverage the abundance of freely accessible trained models to introduce a cost-free approach to model merging.
Gradient-based Class Weighting for Unsupervised Domain Adaptation in Dense Prediction Visual Tasks
In unsupervised domain adaptation (UDA), where models are trained on source data (e. g., synthetic) and adapted to target data (e. g., real-world) without target annotations, addressing the challenge of significant class imbalance remains an open issue.
Open-Vocabulary Attention Maps with Token Optimization for Semantic Segmentation in Diffusion Models
This approach limits the generation of segmentation masks derived from word tokens not contained in the text prompt.
The Robust Semantic Segmentation UNCV2023 Challenge Results
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023.
Soft labelling for semantic segmentation: Bringing coherence to label down-sampling
To that aim, we propose a novel framework for label down-sampling via soft-labeling that better conserves label information after down-sampling.
Ranked #11 on
Semantic Segmentation
on Cityscapes val
