71,693 research outputs found
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
Unsupervised Adaptive Re-identification in Open World Dynamic Camera Networks
Person re-identification is an open and challenging problem in computer
vision. Existing approaches have concentrated on either designing the best
feature representation or learning optimal matching metrics in a static setting
where the number of cameras are fixed in a network. Most approaches have
neglected the dynamic and open world nature of the re-identification problem,
where a new camera may be temporarily inserted into an existing system to get
additional information. To address such a novel and very practical problem, we
propose an unsupervised adaptation scheme for re-identification models in a
dynamic camera network. First, we formulate a domain perceptive
re-identification method based on geodesic flow kernel that can effectively
find the best source camera (already installed) to adapt with a newly
introduced target camera, without requiring a very expensive training phase.
Second, we introduce a transitive inference algorithm for re-identification
that can exploit the information from best source camera to improve the
accuracy across other camera pairs in a network of multiple cameras. Extensive
experiments on four benchmark datasets demonstrate that the proposed approach
significantly outperforms the state-of-the-art unsupervised learning based
alternatives whilst being extremely efficient to compute.Comment: CVPR 2017 Spotligh
Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation
The rapid proliferation of new users and items on the social web has
aggravated the gray-sheep user/long-tail item challenge in recommender systems.
Historically, cross-domain co-clustering methods have successfully leveraged
shared users and items across dense and sparse domains to improve inference
quality. However, they rely on shared rating data and cannot scale to multiple
sparse target domains (i.e., the one-to-many transfer setting). This, combined
with the increasing adoption of neural recommender architectures, motivates us
to develop scalable neural layer-transfer approaches for cross-domain learning.
Our key intuition is to guide neural collaborative filtering with
domain-invariant components shared across the dense and sparse domains,
improving the user and item representations learned in the sparse domains. We
leverage contextual invariances across domains to develop these shared modules,
and demonstrate that with user-item interaction context, we can learn-to-learn
informative representation spaces even with sparse interaction data. We show
the effectiveness and scalability of our approach on two public datasets and a
massive transaction dataset from Visa, a global payments technology company
(19% Item Recall, 3x faster vs. training separate models for each domain). Our
approach is applicable to both implicit and explicit feedback settings.Comment: SIGIR 202
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