2 research outputs found
Multi-label Image Classification using Adaptive Graph Convolutional Networks: from a Single Domain to Multiple Domains
This paper proposes an adaptive graph-based approach for multi-label image
classification. Graph-based methods have been largely exploited in the field of
multi-label classification, given their ability to model label correlations.
Specifically, their effectiveness has been proven not only when considering a
single domain but also when taking into account multiple domains. However, the
topology of the used graph is not optimal as it is pre-defined heuristically.
In addition, consecutive Graph Convolutional Network (GCN) aggregations tend to
destroy the feature similarity. To overcome these issues, an architecture for
learning the graph connectivity in an end-to-end fashion is introduced. This is
done by integrating an attention-based mechanism and a similarity-preserving
strategy. The proposed framework is then extended to multiple domains using an
adversarial training scheme. Numerous experiments are reported on well-known
single-domain and multi-domain benchmarks. The results demonstrate that our
approach achieves competitive results in terms of mean Average Precision (mAP)
and model size as compared to the state-of-the-art. The code will be made
publicly available
Discriminator-free Unsupervised Domain Adaptation for Multi-label Image Classification
In this paper, a discriminator-free adversarial-based Unsupervised Domain
Adaptation (UDA) for Multi-Label Image Classification (MLIC) referred to as
DDA-MLIC is proposed. Over the last two years, some attempts have been made for
introducing adversarial-based UDA methods in the context of MLIC. However,
these methods which rely on an additional discriminator subnet present two
shortcomings. First, the learning of domain-invariant features may harm their
task-specific discriminative power, since the classification and discrimination
tasks are decoupled. Moreover, the use of an additional discriminator usually
induces an increase of the network size. Herein, we propose to overcome these
issues by introducing a novel adversarial critic that is directly deduced from
the task-specific classifier. Specifically, a two-component Gaussian Mixture
Model (GMM) is fitted on the source and target predictions, allowing the
distinction of two clusters. This allows extracting a Gaussian distribution for
each component. The resulting Gaussian distributions are then used for
formulating an adversarial loss based on a Frechet distance. The proposed
method is evaluated on three multi-label image datasets. The obtained results
demonstrate that DDA-MLIC outperforms existing state-of-the-art methods while
requiring a lower number of parameters