354 research outputs found
Cross-Dataset Person Re-Identification via Unsupervised Pose Disentanglement and Adaptation
Person re-identification (re-ID) aims at recognizing the same person from
images taken across different cameras. To address this challenging task,
existing re-ID models typically rely on a large amount of labeled training
data, which is not practical for real-world applications. To alleviate this
limitation, researchers now targets at cross-dataset re-ID which focuses on
generalizing the discriminative ability to the unlabeled target domain when
given a labeled source domain dataset. To achieve this goal, our proposed Pose
Disentanglement and Adaptation Network (PDA-Net) aims at learning deep image
representation with pose and domain information properly disentangled. With the
learned cross-domain pose invariant feature space, our proposed PDA-Net is able
to perform pose disentanglement across domains without supervision in
identities, and the resulting features can be applied to cross-dataset re-ID.
Both of our qualitative and quantitative results on two benchmark datasets
confirm the effectiveness of our approach and its superiority over the
state-of-the-art cross-dataset Re-ID approaches.Comment: Accepted to ICCV 201
Unsupervised Disentanglement GAN for Domain Adaptive Person Re-Identification
While recent person re-identification (ReID) methods achieve high accuracy in
a supervised setting, their generalization to an unlabelled domain is still an
open problem. In this paper, we introduce a novel unsupervised disentanglement
generative adversarial network (UD-GAN) to address the domain adaptation issue
of supervised person ReID. Our framework jointly trains a ReID network for
discriminative features extraction in a source labelled domain using identity
annotation, and adapts the ReID model to an unlabelled target domain by
learning disentangled latent representations on the domain. Identity-unrelated
features in the target domain are distilled from the latent features. As a
result, the ReID features better encompass the identity of a person in the
unsupervised domain. We conducted experiments on the Market1501, DukeMTMC and
MSMT17 datasets. Results show that the unsupervised domain adaptation problem
in ReID is very challenging. Nevertheless, our method shows improvement in half
of the domain transfers and achieve state-of-the-art performance for one of
them.Comment: 8 pages, 5 figures, submitted to ICPR 202
Learning to Generalize over Subpartitions for Heterogeneity-aware Domain Adaptive Nuclei Segmentation
Annotation scarcity and cross-modality/stain data distribution shifts are two
major obstacles hindering the application of deep learning models for nuclei
analysis, which holds a broad spectrum of potential applications in digital
pathology. Recently, unsupervised domain adaptation (UDA) methods have been
proposed to mitigate the distributional gap between different imaging
modalities for unsupervised nuclei segmentation in histopathology images.
However, existing UDA methods are built upon the assumption that data
distributions within each domain should be uniform. Based on the
over-simplified supposition, they propose to align the histopathology target
domain with the source domain integrally, neglecting severe intra-domain
discrepancy over subpartitions incurred by mixed cancer types and sampling
organs. In this paper, for the first time, we propose to explicitly consider
the heterogeneity within the histopathology domain and introduce open compound
domain adaptation (OCDA) to resolve the crux. In specific, a two-stage
disentanglement framework is proposed to acquire domain-invariant feature
representations at both image and instance levels. The holistic design
addresses the limitations of existing OCDA approaches which struggle to capture
instance-wise variations. Two regularization strategies are specifically
devised herein to leverage the rich subpartition-specific characteristics in
histopathology images and facilitate subdomain decomposition. Moreover, we
propose a dual-branch nucleus shape and structure preserving module to prevent
nucleus over-generation and deformation in the synthesized images. Experimental
results on both cross-modality and cross-stain scenarios over a broad range of
diverse datasets demonstrate the superiority of our method compared with
state-of-the-art UDA and OCDA methods
Domain Generalization -- A Causal Perspective
Machine learning models rely on various assumptions to attain high accuracy.
One of the preliminary assumptions of these models is the independent and
identical distribution, which suggests that the train and test data are sampled
from the same distribution. However, this assumption seldom holds in the real
world due to distribution shifts. As a result models that rely on this
assumption exhibit poor generalization capabilities. Over the recent years,
dedicated efforts have been made to improve the generalization capabilities of
these models collectively known as -- \textit{domain generalization methods}.
The primary idea behind these methods is to identify stable features or
mechanisms that remain invariant across the different distributions. Many
generalization approaches employ causal theories to describe invariance since
causality and invariance are inextricably intertwined. However, current surveys
deal with the causality-aware domain generalization methods on a very
high-level. Furthermore, we argue that it is possible to categorize the methods
based on how causality is leveraged in that method and in which part of the
model pipeline is it used. To this end, we categorize the causal domain
generalization methods into three categories, namely, (i) Invariance via Causal
Data Augmentation methods which are applied during the data pre-processing
stage, (ii) Invariance via Causal representation learning methods that are
utilized during the representation learning stage, and (iii) Invariance via
Transferring Causal mechanisms methods that are applied during the
classification stage of the pipeline. Furthermore, this survey includes
in-depth insights into benchmark datasets and code repositories for domain
generalization methods. We conclude the survey with insights and discussions on
future directions
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