131 research outputs found
Bias in Deep Learning and Applications to Face Analysis
Deep learning has fostered the progress in the field of face analysis, resulting in the integration of these models in multiple aspects of society. Even though the majority of research has focused on optimizing standard evaluation metrics, recent work has exposed the bias of such algorithms as well as the dangers of their unaccountable utilization.n this thesis, we explore the bias of deep learning models in the discriminative and the generative setting. We begin by investigating the bias of face analysis models with regards to different demographics. To this end, we collect KANFace, a large-scale video and image dataset of faces captured ``in-the-wild’'. The rich set of annotations allows us to expose the demographic bias of deep learning models, which we mitigate by utilizing adversarial learning to debias the deep representations. Furthermore, we explore neural augmentation as a strategy towards training fair classifiers. We propose a style-based multi-attribute transfer framework that is able to synthesize photo-realistic faces of the underrepresented demographics. This is achieved by introducing a multi-attribute extension to Adaptive Instance Normalisation that captures the multiplicative interactions between the representations of different attributes. Focusing on bias in gender recognition, we showcase the efficacy of the framework in training classifiers that are more fair compared to generative and fairness-aware methods.In the second part, we focus on bias in deep generative models. In particular, we start by studying the generalization of generative models on images of unseen attribute combinations. To this end, we extend the conditional Variational Autoencoder by introducing a multilinear conditioning framework. The proposed method is able to synthesize unseen attribute combinations by modeling the multiplicative interactions between the attributes. Lastly, in order to control protected attributes, we investigate controlled image generation without training on a labelled dataset. We leverage pre-trained Generative Adversarial Networks that are trained in an unsupervised fashion and exploit the clustering that occurs in the representation space of intermediate layers of the generator. We show that these clusters capture semantic attribute information and condition image synthesis on the cluster assignment using Implicit Maximum Likelihood Estimation.Open Acces
Improving CNN-based Person Re-identification using score Normalization
Person re-identification (PRe-ID) is a crucial task in security,
surveillance, and retail analysis, which involves identifying an individual
across multiple cameras and views. However, it is a challenging task due to
changes in illumination, background, and viewpoint. Efficient feature
extraction and metric learning algorithms are essential for a successful PRe-ID
system. This paper proposes a novel approach for PRe-ID, which combines a
Convolutional Neural Network (CNN) based feature extraction method with
Cross-view Quadratic Discriminant Analysis (XQDA) for metric learning.
Additionally, a matching algorithm that employs Mahalanobis distance and a
score normalization process to address inconsistencies between camera scores is
implemented. The proposed approach is tested on four challenging datasets,
including VIPeR, GRID, CUHK01, and PRID450S, and promising results are
obtained. For example, without normalization, the rank-20 rate accuracies of
the GRID, CUHK01, VIPeR and PRID450S datasets were 61.92%, 83.90%, 92.03%,
96.22%; however, after score normalization, they have increased to 64.64%,
89.30%, 92.78%, and 98.76%, respectively. Accordingly, the promising results on
four challenging datasets indicate the effectiveness of the proposed approach.Comment: 5 pages, 6 figures and 2 table
Robust Discriminative Metric Learning for Image Representation
Metric learning has attracted significant attentions in the past decades, for the appealing advances in various realworld applications such as person re-identification and face recognition. Traditional supervised metric learning attempts to seek a discriminative metric, which could minimize the pairwise distance of within-class data samples, while maximizing the pairwise distance of data samples from various classes. However, it is still a challenge to build a robust and discriminative metric, especially for corrupted data in the real-world application. In this paper, we propose a Robust Discriminative Metric Learning algorithm (RDML) via fast low-rank representation and denoising strategy. To be specific, the metric learning problem is guided by a discriminative regularization by incorporating the pair-wise or class-wise information. Moreover, low-rank basis learning is jointly optimized with the metric to better uncover the global data structure and remove noise. Furthermore, fast low-rank representation is implemented to mitigate the computational burden and make sure the scalability on large-scale datasets. Finally, we evaluate our learned metric on several challenging tasks, e.g., face recognition/verification, object recognition, and image clustering. The experimental results verify the effectiveness of the proposed algorithm by comparing to many metric learning algorithms, even deep learning ones
Computer Vision for Multimedia Geolocation in Human Trafficking Investigation: A Systematic Literature Review
The task of multimedia geolocation is becoming an increasingly essential
component of the digital forensics toolkit to effectively combat human
trafficking, child sexual exploitation, and other illegal acts. Typically,
metadata-based geolocation information is stripped when multimedia content is
shared via instant messaging and social media. The intricacy of geolocating,
geotagging, or finding geographical clues in this content is often overly
burdensome for investigators. Recent research has shown that contemporary
advancements in artificial intelligence, specifically computer vision and deep
learning, show significant promise towards expediting the multimedia
geolocation task. This systematic literature review thoroughly examines the
state-of-the-art leveraging computer vision techniques for multimedia
geolocation and assesses their potential to expedite human trafficking
investigation. This includes a comprehensive overview of the application of
computer vision-based approaches to multimedia geolocation, identifies their
applicability in combating human trafficking, and highlights the potential
implications of enhanced multimedia geolocation for prosecuting human
trafficking. 123 articles inform this systematic literature review. The
findings suggest numerous potential paths for future impactful research on the
subject
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