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Visual Learning in Limited-Label Regime.
PhD ThesesAbstract
Deep learning algorithms and architectures have greatly advanced the state-of-the-art in a
wide variety of computer vision tasks, such as object recognition and image retrieval. To achieve
human- or even super-human-level performance in most visual recognition tasks, large collections
of labelled data are generally required to formulate meaningful supervision signals for
model training. The standard supervised learning paradigm, however, is undesired in several perspectives.
First, constructing large-scale labelled datasets not only requires exhaustive manual
annotation efforts, but may also be legally prohibited. Second, deep neural networks trained with
full label supervision upon a limited amount of labelled data are weak at generalising to new
unseen data captured from a different data distribution. This thesis targets at solving the critical
problem of lacking sufficient label annotations in deep learning. More specifically, we investigate
four different deep learning paradigms in limited-label regime, including close-set semisupervised
learning, open-set semi-supervised learning, open-set cross-domain learning, and
unsupervised learning. The former two paradigms are explored in visual classification, which
aims to recognise different categories in the images; while the latter two paradigms are studied in
visual search – particularly in person re-identification – which targets at discriminating different
but similar persons in a finer-grained manner and can be extended to the discrimination of other
objects of high visual similarities. We detail our studies of these paradigms as follows.
Chapter 3: Close-Set Semi-Supervised Learning (Figure 1 (I)) is a fundamental semi-supervised
learning paradigm that aims to learn from a small set of labelled data and a large set of unlabelled
data, where the two sets are assumed to lie in the same label space. To address this problem, existing
semi-supervised deep learning methods often rely on the up-to-date “network-in-training”
to formulate the semi-supervised learning objective, which ignores both the disriminative feature
representation and the model inference uncertainty revealed by the network in the preceding
learning iterations, referred to as the memory of model learning. In this work, we proposed to
augment the deep neural network with a lightweight memory mechanism [Chen et al., 2018b],
which captures the underlying manifold structure of the labelled data at the per-class level, and
further imposes auxiliary unsupervised constraints to fit the unlabelled data towards the underlying
manifolds. This work established a simple yet efficient close-set semi-supervised deep
learning scheme to boost model generalisation in visual classification by learning from sparsely
labelled data and abundant unlabelled data.
Chapter 4: Open-Set Semi-Supervised Learning (Figure 1 (II)) further explores the potential
of learning from abundant noisy unlabelled data, While existing SSL methods artificially assume
that small labelled data and large unlabelled data are drawn from the same class distribution, we
consider a more realistic and uncurated open-set semi-supervised learning paradigm. Considering
visual data is always growing in many visual recognition tasks, it is therefore implausible to
pre-define a fixed label space for the unlabelled data in advance. To investigate this new chal4
Limited-Label
Regime
Same Label Space
Labelled
Data Pool
Unlabelled
Data Pool
(I) Close-Set Semi-Supervised Learning
Propagate Label
Chapter 3
(II) Open-Set Semi-Supervised Learning
Labelled
Data Pool
Unlabelled
Partial Shared Data Pool
Label Space
Selectively Propagate Label
(III) Open-Set Cross-Domain Learning
Labelled
Data Pool
Unlabelled
Data Pool
Disjoint
Label Space
& Domains
Transfer Label
[Chen et al. ICCV19]
Unknown Label Space
Unlabelled Data Pool
Discover Label
[Chen et al. BMVC18]
(IV) Unsupervised Learning
Chapter 4
Chapter 6 Chapter 5
[Chen et al. ECCV18] [Chen et al. AAAI20]
Figure 1: An overview of the main studies in this thesis, which covers four different deep learning
paradigms in the limited-label regime, including (I) close-set semi-supervised learning (Chapter
3), (II) open-set semi-supervised learning (Chapter 4), (III) open-set cross-domain learning
(Chapter 5), and (IV) unsupervised learning (Chapter 6). Each chapter studies a specific deep
learning paradigm that requires to propagate, selectively propagate, transfer, or discover label
information for model optimisation, so as to minimise the manual efforts for label annotations.
While the former two paradigms focus on semi-supervised learning for visual classification, i.e.
recognising different visual categories; the latter two paradigms focus on semi-supervised and
unsupervised learning for visual search, i.e. discriminating different instances such as persons.
lenging learning paradigm, we established the first systematic work to tackle the open-set semisupervised
learning problem in visual classification by a novel approach: uncertainty-aware selfdistillation
[Chen et al., 2020b], which selectively propagates the soft label assignments on the
unlabelled visual data for model optimisation. Built upon an accumulative ensembling strategy,
our approach can jointly capture the model uncertainty to discard out-of-distribution samples,
and propagate less overconfident label assignments on the unlabelled data to avoid catastrophic
error propagation. As one of the pioneers to explore this learning paradigm, this work opens up
new avenues for research in more realistic semi-supervised learning scenarios.
Chapter 5: Open-Set Cross-Domain Learning (Figure 1 (III)) is a challenging semi-supervised
learning paradigm of great practical value. When training a visual recognition model in an operating
visual environment (i.e. source domain, such as the laboratory, simulation, or known scene),
and then deploying it to unknown real-world scenes (i.e. target domain), it is likely that the
model would fail to generalise well in the unseen visual target domain, especially when the target
domain data comes from a disjoint label space with heterogeneous domain drift. Unlike prior
works in domain adaptation that mostly consider a shared label space across two domains, we
studied the more demanding open-set domain adaptation problem, where both label spaces and
domains are disjoint across the labelled and unlabelled datasets. To learn from these heterogeneous
datasets, we designed a novel domain context rendering scheme for open-set cross-domain
learning in visual search [Chen et al., 2019a] – particularly for person re-identification, i.e. a realistic
testbed to evaluate the representational power of fine-grained discrimination among very
similar instances. Our key idea is to transfer the source identity labels into diverse target domain
5
contexts. Our approach enables the generation of an abundant amount of synthetic training data
that selectively blend label information from source domain and context information from target
domain. By training upon such synthetic data, our model can learn a more identity-discriminative
and context-invariant representation for effective visual search in the target domain. This work
sets a new state-of-the-art in cross-domain person re-identification and provides a novel and
generic solution for open-set domain adaptation.
Chapter 6: Unsupervised Learning (Figure 1 (IV)) considers the learning scenario with none
labelled data. In this work, we explore unsupervised learning in visual search, particularly for
person re-identification, a realistic testbed to study unsupervised learning, where person identity
labels are generally very difficult to acquire over a wide surveillance space [Chen et al., 2018a].
In contrast to existing methods in person re-identification that requires exhaustive manual efforts
for labelling cross-view pairwise data, we aims to learn visual representations without using any
manual labels. Our generic rationale is to formulate auxiliary supervision signals that learn to
uncover the underlying data distribution, consequently grouping the visual data in a meaningful
and structural way. To learn from the unlabelled data in a fully unsupervised manner, we proposed
a novel deep association learning scheme to uncover the underlying data-to-data association.
Specifically, two unsupervised constraints – temporal consistency and cycle consistency –
are formulated upon neighbourhood consistency to progressively associate visual features within
and across video sequences of tracked persons. This work sets the new state-of-the-art in videobased
unsupervised person re-identification and advances the automatic exploitation of video
data in real-world surveillance.
In summary, the goal of all these studies is to build efficient and scalable visual learning
models in the limited-label regime, which empower to learn more powerful and reliable representations
from complex unlabelled visual data and consequently learn more powerful visual
representations to facilitate better visual recognition and visual search
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