Towards Better Image Embeddings Using Neural Networks

The primary focus of this dissertation is to study image embeddings extracted by neural networks. Deep Learning (DL) is preferred over traditional Machine Learning (ML) for the reason that feature representations can be automatically constructed from data without human involvement. On account of the effectiveness of deep features, the last decade has witnessed unprecedented advances in Computer Vision (CV), and more real-world applications are expected to be introduced in the coming years. A diverse collection of studies has been included, covering areas such as person re-identification, vehicle attribute recognition, neural image compression, clustering and unsupervised anomaly detection. More specifically, three aspects of feature representations have been thoroughly analyzed. Firstly, features should be distinctive, i.e., features of samples from distinct categories ought to differ significantly. Extracting distinctive features is essential for image retrieval systems, in which an algorithm finds the gallery sample that is closest to a query sample. Secondly, features should be privacy-preserving, i.e., inferring sensitive information from features must be infeasible. With the widespread adoption of Machine Learning as a Service (MLaaS), utilizing privacy-preserving features prevents privacy violations even if the server has been compromised. Thirdly, features should be compressible, i.e., compact features are preferable as they require less storage space. Obtaining compressible features plays a vital role in data compression. Towards the goal of deriving distinctive, privacy-preserving and compressible feature representations, research articles included in this dissertation reveal different approaches to improving image embeddings learned by neural networks. This topic remains a fundamental challenge in Machine Learning, and further research is needed to gain a deeper understanding

Associative learning in baboons and humans: Species differences in learned attention to visual features

We examined attention shifting in baboons and humans during the learning of visual categories. Within a conditional matching-to-sample task, participants of the two species sequentially learned two two-feature categories which shared a common feature. Results showed that humans encoded both features of the initially learned category, but predominantly only the distinctive feature of the subsequently learned category. Although baboons initially encoded both features of the first category, they ultimately retained only the distinctive features of each category. Empirical data from the two species were analyzed with the 1996 ADIT connectionist model of Kruschke. ADIT fits the baboon data when the attentional shift rate is zero, and the human data when the attentional shift rate is not zero. These empirical and modeling results suggest species differences in learned attention to visual features

Cognitive consequences of perceiving social exclusion

Although a great deal is now known about how people mentally represent individuals and groups, less attention has been paid to the question of how interpersonal relationships are represented in memory. Drawing on principles of categorization, this paper reports an investigation into how we mentally represent the relationships of others. In three experiments, evidence for assimilation effects following social exclusion (and subsequent categorization) is found. Experiment 1 uses a judgment paradigm to demonstrate that social exclusion influences the perception of interpersonal closeness. Experiments 2 and 3 employ a memory confusion paradigm to establish that representations of relationship partners are assimilated following the exclusion of a third party. © 2008 Elsevier Inc. All rights reserved

Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks

Place recognition is an essential component of Simultaneous Localization And Mapping (SLAM). Under severe appearance change, reliable place recognition is a difficult perception task since the same place is perceptually very different in the morning, at night, or over different seasons. This work addresses place recognition as a domain translation task. Using a pair of coupled Generative Adversarial Networks (GANs), we show that it is possible to generate the appearance of one domain (such as summer) from another (such as winter) without requiring image-to-image correspondences across the domains. Mapping between domains is learned from sets of images in each domain without knowing the instance-to-instance correspondence by enforcing a cyclic consistency constraint. In the process, meaningful feature spaces are learned for each domain, the distances in which can be used for the task of place recognition. Experiments show that learned features correspond to visual similarity and can be effectively used for place recognition across seasons.Comment: Accepted for publication in IEEE International Conference on Robotics and Automation (ICRA), 201

LIFT: Learned Invariant Feature Transform

We introduce a novel Deep Network architecture that implements the full feature point handling pipeline, that is, detection, orientation estimation, and feature description. While previous works have successfully tackled each one of these problems individually, we show how to learn to do all three in a unified manner while preserving end-to-end differentiability. We then demonstrate that our Deep pipeline outperforms state-of-the-art methods on a number of benchmark datasets, without the need of retraining.Comment: Accepted to ECCV 2016 (spotlight