Domain Adaptation and Privileged Information for Visual Recognition

The automatic identification of entities like objects, people or their actions in visual data, such as images or video, has significantly improved, and is now being deployed in access control, social media, online retail, autonomous vehicles, and several other applications. This visual recognition capability leverages supervised learning techniques, which require large amounts of labeled training data from the target distribution representative of the particular task at hand. However, collecting such training data might be expensive, require too much time, or even be impossible. In this work, we introduce several novel approaches aiming at compensating for the lack of target training data. Rather than leveraging prior knowledge for building task-specific models, typically easier to train, we focus on developing general visual recognition techniques, where the notion of prior knowledge is better identified by additional information, available during training. Depending on the nature of such information, the learning problem may turn into domain adaptation (DA), domain generalization (DG), leaning using privileged information (LUPI), or domain adaptation with privileged information (DAPI).;When some target data samples are available and additional information in the form of labeled data from a different source is also available, the learning problem becomes domain adaptation. Unlike previous DA work, we introduce two novel approaches for the few-shot learning scenario, which require only very few labeled target samples, and even one can be very effective. The first method exploits a Siamese deep neural network architecture for learning an embedding where visual categories from the source and target distributions are semantically aligned and yet maximally separated. The second approach instead, extends adversarial learning to simultaneously maximize the confusion between source and target domains while achieving semantic alignment.;In complete absence of target data, several cheaply available source datasets related to the target distribution can be leveraged as additional information for learning a task. This is the domain generalization setting. We introduce the first deep learning approach to address the DG problem, by extending a Siamese network architecture for learning a representation of visual categories that is invariant with respect to the sources, while imposing semantic alignment and class separation to maximize generalization performance on unseen target domains.;There are situations in which target data for training might come equipped with additional information that can be modeled as an auxiliary view of the data, and that unfortunately is not available during testing. This is the LUPI scenario. We introduce a novel framework based on the information bottleneck that leverages the auxiliary view to improve the performance of visual classifiers. We do so by introducing a formulation that is general, in the sense that can be used with any visual classifier.;Finally, when the available target data is unlabeled, and there is closely related labeled source data, which is also equipped with an auxiliary view as additional information, we pose the question of how to leverage the source data views to train visual classifiers for unseen target data. This is the DAPI scenario. We extend the LUPI framework based on the information bottleneck to learn visual classifiers in DAPI settings and show that privileged information can be leveraged to improve the learning on new domains. Also, the novel DAPI framework is general and can be used with any visual classifier.;Every use of auxiliary information has been validated extensively using publicly available benchmark datasets, and several new state-of-the-art accuracy performance values have been set. Examples of application domains include visual object recognition from RGB images and from depth data, handwritten digit recognition, and gesture recognition from video

Automated Classification of Vowel Category and Speaker Type in the High-Frequency Spectrum

The high-frequency region of vowel signals (above the third formant or F3) has received little research attention. Recent evidence, however, has documented the perceptual utility of high-frequency information in the speech signal above the traditional frequency bandwidth known to contain important cues for speech and speaker recognition. The purpose of this study was to determine if high-pass filtered vowels could be separated by vowel category and speaker type in a supervised learning framework. Mel frequency cepstral coefficients (MFCCs) were extracted from productions of six vowel categories produced by two male, two female, and two child speakers. Results revealed that the filtered vowels were well separated by vowel category and speaker type using MFCCs from the high-frequency spectrum. This demonstrates the presence of useful information for automated classification from the high-frequency region and is the first study to report findings of this nature in a supervised learning framework

ALADIN: All Layer Adaptive Instance Normalization for Fine-grained Style Similarity

We present ALADIN (All Layer AdaIN); a novel architecture for searching images based on the similarity of their artistic style. Representation learning is critical to visual search, where distance in the learned search embedding reflects image similarity. Learning an embedding that discriminates fine-grained variations in style is hard, due to the difficulty of defining and labelling style. ALADIN takes a weakly supervised approach to learning a representation for fine-grained style similarity of digital artworks, leveraging BAM-FG, a novel large-scale dataset of user generated content groupings gathered from the web. ALADIN sets a new state of the art accuracy for style-based visual search over both coarse labelled style data (BAM) and BAM-FG; a new 2.62 million image dataset of 310,000 fine-grained style groupings also contributed by this work

Automated classification of vowel category and speaker type in the high-frequency spectrum

The high-frequency region of vowel signals (above the third formant or F3) has received little research attention. Recent evidence, however, has documented the perceptual utility of high-frequency information in the speech signal above the traditional frequency bandwidth known to contain important cues for speech and speaker recognition. The purpose of this study was to determine if high-pass filtered vowels could be separated by vowel category and speaker type in a supervised learning framework. Mel frequency cepstral coefficients (MFCCs) were extracted from productions of six vowel categories produced by two male, two female, and two child speakers. Results revealed that the filtered vowels were well separated by vowel category and speaker type using MFCCs from the high-frequency spectrum. This demonstrates the presence of useful information for automated classification from the high-frequency region and is the first study to report findings of this nature in a supervised learning framework

ALADIN: All Layer Adaptive Instance Normalization for Fine-grained Style Similarity

We present ALADIN (All Layer AdaIN); a novel architecture for searching images based on the similarity of their artistic style. Representation learning is critical to visual search, where distance in the learned search embedding reflects image similarity. Learning an embedding that discriminates fine-grained variations in style is hard, due to the difficulty of defining and labelling style. ALADIN takes a weakly supervised approach to learning a representation for fine-grained style similarity of digital artworks, leveraging BAM-FG, a novel large-scale dataset of user generated content groupings gathered from the web. ALADIN sets a new state of the art accuracy for style-based visual search over both coarse labelled style data (BAM) and BAM-FG; a new 2.62 million image dataset of 310,000 fine-grained style groupings also contributed by this work

Automated extraction and validation of children\u27s gait parameters with the Kinect

Background: Gait analysis for therapy regimen prescription and monitoring requires patients to physically access clinics with specialized equipment. The timely availability of such infrastructure at the right frequency is especially important for small children. Besides being very costly, this is a challenge for many children living in rural areas. This is why this work develops a low-cost, portable, and automated approach for in-home gait analysis, based on the Microsoft Kinect. Methods: A robust and efficient method for extracting gait parameters is introduced, which copes with the high variability of noisy Kinect skeleton tracking data experienced across the population of young children. This is achieved by temporally segmenting the data with an approach based on coupling a probabilistic matching of stride template models, learned offline, with the estimation of their global and local temporal scaling. A preliminary study conducted on healthy children between 2 and 4 years of age is performed to analyze the accuracy, precision, repeatability, and concurrent validity of the proposed method against the GAITRite when measuring several spatial and temporal children’s gait parameters. Results: The method has excellent accuracy and good precision, with segmenting temporal sequences of body joint locations into stride and step cycles. Also, the spatial and temporal gait parameters, estimated automatically, exhibit good concurrent validity with those provided by the GAITRite, as well as very good repeatability. In particular, on a range of nine gait parameters, the relative and absolute agreements were found to be good and excellent, and the overall agreements were found to be good and moderate. Conclusion: This work enables and validates the automated use of the Kinect for children’s gait analysis in healthy subjects. In particular, the approach makes a step forward towards developing a low-cost, portable, parent-operated in-home tool for clinicians assisting young children