23 research outputs found
Multivariate Regression on the Grassmannian for Predicting Novel Domains
This work was supported by EPSRC (EP/L023385/1), and the European Union’s Horizon 2020 research and innovation program under grant agreement No 640891
Joint Geometrical and Statistical Alignment for Visual Domain Adaptation
This paper presents a novel unsupervised domain adaptation method for
cross-domain visual recognition. We propose a unified framework that reduces
the shift between domains both statistically and geometrically, referred to as
Joint Geometrical and Statistical Alignment (JGSA). Specifically, we learn two
coupled projections that project the source domain and target domain data into
low dimensional subspaces where the geometrical shift and distribution shift
are reduced simultaneously. The objective function can be solved efficiently in
a closed form. Extensive experiments have verified that the proposed method
significantly outperforms several state-of-the-art domain adaptation methods on
a synthetic dataset and three different real world cross-domain visual
recognition tasks
Zero-Shot Deep Domain Adaptation
Domain adaptation is an important tool to transfer knowledge about a task
(e.g. classification) learned in a source domain to a second, or target domain.
Current approaches assume that task-relevant target-domain data is available
during training. We demonstrate how to perform domain adaptation when no such
task-relevant target-domain data is available. To tackle this issue, we propose
zero-shot deep domain adaptation (ZDDA), which uses privileged information from
task-irrelevant dual-domain pairs. ZDDA learns a source-domain representation
which is not only tailored for the task of interest but also close to the
target-domain representation. Therefore, the source-domain task of interest
solution (e.g. a classifier for classification tasks) which is jointly trained
with the source-domain representation can be applicable to both the source and
target representations. Using the MNIST, Fashion-MNIST, NIST, EMNIST, and SUN
RGB-D datasets, we show that ZDDA can perform domain adaptation in
classification tasks without access to task-relevant target-domain training
data. We also extend ZDDA to perform sensor fusion in the SUN RGB-D scene
classification task by simulating task-relevant target-domain representations
with task-relevant source-domain data. To the best of our knowledge, ZDDA is
the first domain adaptation and sensor fusion method which requires no
task-relevant target-domain data. The underlying principle is not particular to
computer vision data, but should be extensible to other domains.Comment: This paper is accepted to the European Conference on Computer Vision
(ECCV), 201
AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs
The ability to categorize is a cornerstone of visual intelligence, and a key
functionality for artificial, autonomous visual machines. This problem will
never be solved without algorithms able to adapt and generalize across visual
domains. Within the context of domain adaptation and generalization, this paper
focuses on the predictive domain adaptation scenario, namely the case where no
target data are available and the system has to learn to generalize from
annotated source images plus unlabeled samples with associated metadata from
auxiliary domains. Our contributionis the first deep architecture that tackles
predictive domainadaptation, able to leverage over the information broughtby
the auxiliary domains through a graph. Moreover, we present a simple yet
effective strategy that allows us to take advantage of the incoming target data
at test time, in a continuous domain adaptation scenario. Experiments on three
benchmark databases support the value of our approach.Comment: CVPR 2019 (oral
AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning
WiFi sensing technology has shown superiority in smart homes among various
sensors for its cost-effective and privacy-preserving merits. It is empowered
by Channel State Information (CSI) extracted from WiFi signals and advanced
machine learning models to analyze motion patterns in CSI. Many learning-based
models have been proposed for kinds of applications, but they severely suffer
from environmental dependency. Though domain adaptation methods have been
proposed to tackle this issue, it is not practical to collect high-quality,
well-segmented and balanced CSI samples in a new environment for adaptation
algorithms, but randomly-captured CSI samples can be easily collected.
{\color{black}In this paper, we firstly explore how to learn a robust model
from these low-quality CSI samples, and propose AutoFi, an annotation-efficient
WiFi sensing model based on a novel geometric self-supervised learning
algorithm.} The AutoFi fully utilizes unlabeled low-quality CSI samples that
are captured randomly, and then transfers the knowledge to specific tasks
defined by users, which is the first work to achieve cross-task transfer in
WiFi sensing. The AutoFi is implemented on a pair of Atheros WiFi APs for
evaluation. The AutoFi transfers knowledge from randomly collected CSI samples
into human gait recognition and achieves state-of-the-art performance.
Furthermore, we simulate cross-task transfer using public datasets to further
demonstrate its capacity for cross-task learning. For the UT-HAR and Widar
datasets, the AutoFi achieves satisfactory results on activity recognition and
gesture recognition without any prior training. We believe that the AutoFi
takes a huge step toward automatic WiFi sensing without any developer
engagement.Comment: The paper has been accepted by IEEE Internet of Things Journa