7 research outputs found
KRADA: Known-region-aware Domain Alignment for Open World Semantic Segmentation
In semantic segmentation, we aim to train a pixel-level classifier to assign
category labels to all pixels in an image, where labeled training images and
unlabeled test images are from the same distribution and share the same label
set. However, in an open world, the unlabeled test images probably contain
unknown categories and have different distributions from the labeled images.
Hence, in this paper, we consider a new, more realistic, and more challenging
problem setting where the pixel-level classifier has to be trained with labeled
images and unlabeled open-world images -- we name it open world semantic
segmentation (OSS). In OSS, the trained classifier is expected to identify
unknown-class pixels and classify known-class pixels well. To solve OSS, we
first investigate which distribution that unknown-class pixels obey. Then,
motivated by the goodness-of-fit test, we use statistical measurements to show
how a pixel fits the distribution of an unknown class and select highly-fitted
pixels to form the unknown region in each image. Eventually, we propose an
end-to-end learning framework, known-region-aware domain alignment (KRADA), to
distinguish unknown classes while aligning distributions of known classes in
labeled and unlabeled open-world images. The effectiveness of KRADA has been
verified on two synthetic tasks and one COVID-19 segmentation task
Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation
Domain adaptation solves image classification problems in the target domain by taking advantage of the labelled source data and unlabelled target data. Usually, the source and target domains share the same set of classes. As a special case, Open-Set Domain Adaptation (OSDA) assumes there exist additional classes in the target domain but are not present in the source domain. To solve such a domain adaptation problem, our proposed method learns discriminative common subspaces for the source and target domains using a novel Open-Set Locality Preserving Projection (OSLPP) algorithm. The source and target domain data are aligned in the learned common spaces class-wise. To handle the open-set classification problem, our method progressively selects target samples to be pseudo-labelled as known classes, rejects the outliers if they are detected as unknown classes, and leaves the remaining target samples as uncertain. The common subspace learning algorithm OSLPP simultaneously aligns the labelled source data and pseudo-labelled target data from known classes and pushes the rejected target data away from the known classes. The common subspace learning and the pseudo-labelled sample selection/rejection facilitate each other in an iterative learning framework and achieve state-of-the-art performance on four benchmark datasets Office-31, Office-Home, VisDA17 and Syn2Real-O with the average HOS of 87.6%, 67.0%, 76.1% and 65.6% respectively
Exploring category-agnostic clusters for open-set domain adaptation
Unsupervised domain adaptation has received significant attention in recent
years. Most of existing works tackle the closed-set scenario, assuming that the
source and target domains share the exactly same categories. In practice,
nevertheless, a target domain often contains samples of classes unseen in
source domain (i.e., unknown class). The extension of domain adaptation from
closed-set to such open-set situation is not trivial since the target samples
in unknown class are not expected to align with the source. In this paper, we
address this problem by augmenting the state-of-the-art domain adaptation
technique, Self-Ensembling, with category-agnostic clusters in target domain.
Specifically, we present Self-Ensembling with Category-agnostic Clusters
(SE-CC) -- a novel architecture that steers domain adaptation with the
additional guidance of category-agnostic clusters that are specific to target
domain. These clustering information provides domain-specific visual cues,
facilitating the generalization of Self-Ensembling for both closed-set and
open-set scenarios. Technically, clustering is firstly performed over all the
unlabeled target samples to obtain the category-agnostic clusters, which reveal
the underlying data space structure peculiar to target domain. A clustering
branch is capitalized on to ensure that the learnt representation preserves
such underlying structure by matching the estimated assignment distribution
over clusters to the inherent cluster distribution for each target sample.
Furthermore, SE-CC enhances the learnt representation with mutual information
maximization. Extensive experiments are conducted on Office and VisDA datasets
for both open-set and closed-set domain adaptation, and superior results are
reported when comparing to the state-of-the-art approaches.Comment: CVPR 202
Open-Set Source-Free Domain Adaptation in Fundus Images Analysis
Unsupervised domain adaptation (UDA) is crucial in medical image analysis where only the source domain data is labeled. There is a lot of emphasis on the closed-set paradigm in UDA, where the label space is assumed to be the same in all domains. However, medical imaging often has an open-world scenario where the source domain has a limited number of disease categories and the target domain has unknown distinct classes. Also, maintaining the privacy of patients is a crucial aspect of medical research and practice. In this work, we shed light on the Open-Set Domain Adaptation (OSDA) setting on fundus image analysis while preserving the privacy concern. In particular, we step towards a source-free open-set domain adaptation where, without source data, the source model is utilized to facilitate adaptation to open-set unlabeled data by delving into channel-wise and local features for fundus disease recognition. In particular, considering the nature of the fundus images, we present a novel objective way in the adaptation phase to utilize spatial and channel-wise information to select the best source model for a target domain, even by considering the small inter-class variation between samples. Our approach has achieved state-of-the-art performance compared to other methods