Multimodal Adversarial Learning

Deep Convolutional Neural Networks (DCNN) have proven to be an exceptional tool for object recognition, generative modelling, and multi-modal learning in various computer vision applications. However, recent findings have shown that such state-of-the-art models can be easily deceived by inserting slight imperceptible perturbations to key pixels in the input. A good target detection systems can accurately identify targets by localizing their coordinates on the input image of interest. This is ideally achieved by labeling each pixel in an image as a background or a potential target pixel. However, prior research still confirms that such state of the art targets models are susceptible to adversarial attacks. In the case of generative models, facial sketches drawn by artists mostly used by law enforcement agencies depend on the ability of the artist to clearly replicate all the key facial features that aid in capturing the true identity of a subject. Recent works have attempted to synthesize these sketches into plausible visual images to improve visual recognition and identification. However, synthesizing photo-realistic images from sketches proves to be an even more challenging task, especially for sensitive applications such as suspect identification. However, the incorporation of hybrid discriminators, which perform attribute classification of multiple target attributes, a quality guided encoder that minimizes the perceptual dissimilarity of the latent space embedding of the synthesized and real image at different layers in the network have shown to be powerful tools towards better multi modal learning techniques. In general, our overall approach was aimed at improving target detection systems and the visual appeal of synthesized images while incorporating multiple attribute assignment to the generator without compromising the identity of the synthesized image. We synthesized sketches using XDOG filter for the CelebA, Multi-modal and CelebA-HQ datasets and from an auxiliary generator trained on sketches from CUHK, IIT-D and FERET datasets. Our results overall for different model applications are impressive compared to current state of the art

Domain Generalization in Vision: A Survey

Generalization to out-of-distribution (OOD) data is a capability natural to humans yet challenging for machines to reproduce. This is because most learning algorithms strongly rely on the i.i.d.~assumption on source/target data, which is often violated in practice due to domain shift. Domain generalization (DG) aims to achieve OOD generalization by using only source data for model learning. Since first introduced in 2011, research in DG has made great progresses. In particular, intensive research in this topic has led to a broad spectrum of methodologies, e.g., those based on domain alignment, meta-learning, data augmentation, or ensemble learning, just to name a few; and has covered various vision applications such as object recognition, segmentation, action recognition, and person re-identification. In this paper, for the first time a comprehensive literature review is provided to summarize the developments in DG for computer vision over the past decade. Specifically, we first cover the background by formally defining DG and relating it to other research fields like domain adaptation and transfer learning. Second, we conduct a thorough review into existing methods and present a categorization based on their methodologies and motivations. Finally, we conclude this survey with insights and discussions on future research directions.Comment: v4: includes the word "vision" in the title; improves the organization and clarity in Section 2-3; adds future directions; and mor

ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalization

Domain generalization (DG) aims to learn a model that generalizes well to unseen target domains utilizing multiple source domains without re-training. Most existing DG works are based on convolutional neural networks (CNNs). However, the local operation of the convolution kernel makes the model focus too much on local representations (e.g., texture), which inherently causes the model more prone to overfit to the source domains and hampers its generalization ability. Recently, several MLP-based methods have achieved promising results in supervised learning tasks by learning global interactions among different patches of the image. Inspired by this, in this paper, we first analyze the difference between CNN and MLP methods in DG and find that MLP methods exhibit a better generalization ability because they can better capture the global representations (e.g., structure) than CNN methods. Then, based on a recent lightweight MLP method, we obtain a strong baseline that outperforms most state-of-the-art CNN-based methods. The baseline can learn global structure representations with a filter to suppress structure irrelevant information in the frequency space. Moreover, we propose a dynAmic LOw-Frequency spectrum Transform (ALOFT) that can perturb local texture features while preserving global structure features, thus enabling the filter to remove structure-irrelevant information sufficiently. Extensive experiments on four benchmarks have demonstrated that our method can achieve great performance improvement with a small number of parameters compared to SOTA CNN-based DG methods. Our code is available at https://github.com/lingeringlight/ALOFT/.Comment: Accepted by CVPR2023. The code is available at https://github.com/lingeringlight/ALOFT

Efficient image duplicate detection based on image analysis

This thesis is about the detection of duplicated images. More precisely, the developed system is able to discriminate possibly modified copies of original images from other unrelated images. The proposed method is referred to as content-based since it relies only on content analysis techniques rather than using image tagging as done in watermarking. The proposed content-based duplicate detection system classifies a test image by associating it with a label that corresponds to one of the original known images. The classification is performed in four steps. In the first step, the test image is described by using global statistics about its content. In the second step, the most likely original images are efficiently selected using a spatial indexing technique called R-Tree. The third step consists in using binary detectors to estimate the probability that the test image is a duplicate of the original images selected in the second step. Indeed, each original image known to the system is associated with an adapted binary detector, based on a support vector classifier, that estimates the probability that a test image is one of its duplicate. Finally, the fourth and last step consists in choosing the most probable original by picking that with the highest estimated probability. Comparative experiments have shown that the proposed content-based image duplicate detector greatly outperforms detectors using the same image description but based on a simpler distance functions rather than using a classification algorithm. Additional experiments are carried out so as to compare the proposed system with existing state of the art methods. Accordingly, it also outperforms the perceptual distance function method, which uses similar statistics to describe the image. While the proposed method is slightly outperformed by the key points method, it is five to ten times less complex in terms of computational requirements. Finally, note that the nature of this thesis is essentially exploratory since it is one of the first attempts to apply machine learning techniques to the relatively recent field of content-based image duplicate detection

Efficient image duplicate detection based on image analysis

This thesis is about the detection of duplicated images. More precisely, the developed system is able to discriminate possibly modified copies of original images from other unrelated images. The proposed method is referred to as content-based since it relies only on content analysis techniques rather than using image tagging as done in watermarking. The proposed content-based duplicate detection system classifies a test image by associating it with a label that corresponds to one of the original known images. The classification is performed in four steps. In the first step, the test image is described by using global statistics about its content. In the second step, the most likely original images are efficiently selected using a spatial indexing technique called R-Tree. The third step consists in using binary detectors to estimate the probability that the test image is a duplicate of the original images selected in the second step. Indeed, each original image known to the system is associated with an adapted binary detector, based on a support vector classifier, that estimates the probability that a test image is one of its duplicate. Finally, the fourth and last step consists in choosing the most probable original by picking that with the highest estimated probability. Comparative experiments have shown that the proposed content-based image duplicate detector greatly outperforms detectors using the same image description but based on a simpler distance functions rather than using a classification algorithm. Additional experiments are carried out so as to compare the proposed system with existing state of the art methods. Accordingly, it also outperforms the perceptual distance function method, which uses similar statistics to describe the image. While the proposed method is slightly outperformed by the key points method, it is five to ten times less complex in terms of computational requirements. Finally, note that the nature of this thesis is essentially exploratory since it is one of the first attempts to apply machine learning techniques to the relatively recent field of content-based image duplicate detection

Three-dimensional Laser-based Classification in Outdoor Environments

Robotics research strives for deploying autonomous systems in populated environments, such as inner city traffic. Autonomous cars need a reliable collision avoidance, but also an object recognition to distinguish different classes of traffic participants. For both tasks, fast three-dimensional laser range sensors generating multiple accurate laser range scans per second, each consisting of a vast number of laser points, are often employed. In this thesis, we investigate and develop classification algorithms that allow us to automatically assign semantic labels to laser scans. We mainly face two challenges: (1) we have to ensure consistent and correct classification results and (2) we must efficiently process a vast number of laser points per scan. In consideration of these challenges, we cover both stages of classification -- the feature extraction from laser range scans and the classification model that maps from the features to semantic labels. As for the feature extraction, we contribute by thoroughly evaluating important state-of-the-art histogram descriptors. We investigate critical parameters of the descriptors and experimentally show for the first time that the classification performance can be significantly improved using a large support radius and a global reference frame. As for learning the classification model, we contribute with new algorithms that improve the classification efficiency and accuracy. Our first approach aims at deriving a consistent point-wise interpretation of the whole laser range scan. By combining efficient similarity-preserving hashing and multiple linear classifiers, we considerably improve the consistency of label assignments, requiring only minimal computational overhead compared to a single linear classifier. In the last part of the thesis, we aim at classifying objects represented by segments. We propose a novel hierarchical segmentation approach comprising multiple stages and a novel mixture classification model of multiple bag-of-words vocabularies. We demonstrate superior performance of both approaches compared to their single component counterparts using challenging real world datasets.Ziel des Forschungsbereichs Robotik ist der Einsatz autonomer Systeme in natürlichen Umgebungen, wie zum Beispiel innerstädtischem Verkehr. Autonome Fahrzeuge benötigen einerseits eine zuverlässige Kollisionsvermeidung und andererseits auch eine Objekterkennung zur Unterscheidung verschiedener Klassen von Verkehrsteilnehmern. Verwendung finden vorallem drei-dimensionale Laserentfernungssensoren, die mehrere präzise Laserentfernungsscans pro Sekunde erzeugen und jeder Scan besteht hierbei aus einer hohen Anzahl an Laserpunkten. In dieser Dissertation widmen wir uns der Untersuchung und Entwicklung neuartiger Klassifikationsverfahren zur automatischen Zuweisung von semantischen Objektklassen zu Laserpunkten. Hierbei begegnen wir hauptsächlich zwei Herausforderungen: (1) wir möchten konsistente und korrekte Klassifikationsergebnisse erreichen und (2) die immense Menge an Laserdaten effizient verarbeiten. Unter Berücksichtigung dieser Herausforderungen untersuchen wir beide Verarbeitungsschritte eines Klassifikationsverfahrens -- die Merkmalsextraktion unter Nutzung von Laserdaten und das eigentliche Klassifikationsmodell, welches die Merkmale auf semantische Objektklassen abbildet. Bezüglich der Merkmalsextraktion leisten wir ein Beitrag durch eine ausführliche Evaluation wichtiger Histogrammdeskriptoren. Wir untersuchen kritische Deskriptorparameter und zeigen zum ersten Mal, dass die Klassifikationsgüte unter Nutzung von großen Merkmalsradien und eines globalen Referenzrahmens signifikant gesteigert wird. Bezüglich des Lernens des Klassifikationsmodells, leisten wir Beiträge durch neue Algorithmen, welche die Effizienz und Genauigkeit der Klassifikation verbessern. In unserem ersten Ansatz möchten wir eine konsistente punktweise Interpretation des gesamten Laserscans erreichen. Zu diesem Zweck kombinieren wir eine ähnlichkeitserhaltende Hashfunktion und mehrere lineare Klassifikatoren und erreichen hierdurch eine erhebliche Verbesserung der Konsistenz der Klassenzuweisung bei minimalen zusätzlichen Aufwand im Vergleich zu einem einzelnen linearen Klassifikator. Im letzten Teil der Dissertation möchten wir Objekte, die als Segmente repräsentiert sind, klassifizieren. Wir stellen eine neuartiges hierarchisches Segmentierungsverfahren und ein neuartiges Klassifikationsmodell auf Basis einer Mixtur mehrerer bag-of-words Vokabulare vor. Wir demonstrieren unter Nutzung von praxisrelevanten Datensätzen, dass beide Ansätze im Vergleich zu ihren Entsprechungen aus einer einzelnen Komponente zu erheblichen Verbesserungen führen

Large-scale Content-based Visual Information Retrieval

Rather than restricting search to the use of metadata, content-based information retrieval methods attempt to index, search and browse digital objects by means of signatures or features describing their actual content. Such methods have been intensively studied in the multimedia community to allow managing the massive amount of raw multimedia documents created every day (e.g. video will account to 84% of U.S. internet traffic by 2018). Recent years have consequently witnessed a consistent growth of content-aware and multi-modal search engines deployed on massive multimedia data. Popular multimedia search applications such as Google images, Youtube, Shazam, Tineye or MusicID clearly demonstrated that the first generation of large-scale audio-visual search technologies is now mature enough to be deployed on real-world big data. All these successful applications did greatly benefit from 15 years of research on multimedia analysis and efficient content-based indexing techniques. Yet the maturity reached by the first generation of content-based search engines does not preclude an intensive research activity in the field. There is actually still a lot of hard problems to be solved before we can retrieve any information in images or sounds as easily as we do in text documents. Content-based search methods actually have to reach a finer understanding of the contents as well as a higher semantic level. This requires modeling the raw signals by more and more complex and numerous features, so that the algorithms for analyzing, indexing and searching such features have to evolve accordingly. This thesis describes several of my works related to large-scale content-based information retrieval. The different contributions are presented in a bottom-up fashion reflecting a typical three-tier software architecture of an end-to-end multimedia information retrieval system. The lowest layer is only concerned with managing, indexing and searching large sets of high-dimensional feature vectors, whatever their origin or role in the upper levels (visual or audio features, global or part-based descriptions, low or high semantic level, etc. ). The middle layer rather works at the document level and is in charge of analyzing, indexing and searching collections of documents. It typically extracts and embeds the low-level features, implements the querying mechanisms and post-processes the results returned by the lower layer. The upper layer works at the applicative level and is in charge of providing useful and interactive functionalities to the end-user. It typically implements the front-end of the search application, the crawler and the orchestration of the different indexing and search services