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Found 13 papers, 2 papers with code
Learning Classifiers of Prototypes and Reciprocal Points for Universal Domain Adaptation
To successfully train our framework, we collect the partial, confident target samples that are classified as known or unknown through on our proposed multi-criteria selection.
CD-TTA: Compound Domain Test-time Adaptation for Semantic Segmentation
Test-time adaptation (TTA) has attracted significant attention due to its practical properties which enable the adaptation of a pre-trained model to a new domain with only target dataset during the inference stage.
MATE: Masked Autoencoders are Online 3D Test-Time Learners
Like existing TTT methods, which focused on classifying 2D images in the presence of distribution shifts at test-time, MATE also leverages test data for adaptation.
Moving from 2D to 3D: volumetric medical image classification for rectal cancer staging
In this study, we present a volumetric convolutional neural network to accurately discriminate T2 from T3 stage rectal cancer with rectal MR volumes.
MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation
In this paper, we propose and explore a new multi-modal extension of test-time adaptation for 3D semantic segmentation.
UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation
Inspired by recent multi-modal UDA techniques, the proposed method exploits a teacher-student self-supervised learning scheme to train a pose estimation network without using target domain pose labels.
Ranked #1 on
6D Pose Estimation using RGBD
on REAL275
(mAP 10, 2cm metric)
Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation
The scheme first clusters compound target data based on style, discovering multiple latent domains (discover).
LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentation
In order to find the uncertain points, we generate an inconsistency mask using the proposed adaptive pixel selector and we label these segment-based regions to achieve near supervised performance with only a small fraction (about 2. 2%) ground truth points, which we call "Segment based Pixel-Labeling (SPL)".
Unsupervised Domain Adaptation for Video Semantic Segmentation
In this work, we present a new video extension of this task, namely Unsupervised Domain Adaptation for Video Semantic Segmentation.
Learning Representations by Contrasting Clusters While Bootstrapping Instances
Learning visual representations using large-scale unlabelled images is a holy grail for most of computer vision tasks.
Two-phase Pseudo Label Densification for Self-training based Domain Adaptation
However, since only the confident predictions are taken as pseudo labels, existing self-training approaches inevitably produce sparse pseudo labels in practice.
Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
Finally, to decrease the intra-domain gap, we propose to employ a self-supervised adaptation technique from the easy to the hard split.
Ranked #2 on
Domain Adaptation
on Synscapes-to-Cityscapes
Image-to-Image Translation via Group-wise Deep Whitening-and-Coloring Transformation
However, applying this approach in image translation is computationally intensive and error-prone due to the expensive time complexity and its non-trivial backpropagation.
