30,975 research outputs found

    Towards information profiling: data lake content metadata management

    Get PDF
    There is currently a burst of Big Data (BD) processed and stored in huge raw data repositories, commonly called Data Lakes (DL). These BD require new techniques of data integration and schema alignment in order to make the data usable by its consumers and to discover the relationships linking their content. This can be provided by metadata services which discover and describe their content. However, there is currently a lack of a systematic approach for such kind of metadata discovery and management. Thus, we propose a framework for the profiling of informational content stored in the DL, which we call information profiling. The profiles are stored as metadata to support data analysis. We formally define a metadata management process which identifies the key activities required to effectively handle this.We demonstrate the alternative techniques and performance of our process using a prototype implementation handling a real-life case-study from the OpenML DL, which showcases the value and feasibility of our approach.Peer ReviewedPostprint (author's final draft

    Improving Landmark Localization with Semi-Supervised Learning

    Full text link
    We present two techniques to improve landmark localization in images from partially annotated datasets. Our primary goal is to leverage the common situation where precise landmark locations are only provided for a small data subset, but where class labels for classification or regression tasks related to the landmarks are more abundantly available. First, we propose the framework of sequential multitasking and explore it here through an architecture for landmark localization where training with class labels acts as an auxiliary signal to guide the landmark localization on unlabeled data. A key aspect of our approach is that errors can be backpropagated through a complete landmark localization model. Second, we propose and explore an unsupervised learning technique for landmark localization based on having a model predict equivariant landmarks with respect to transformations applied to the image. We show that these techniques, improve landmark prediction considerably and can learn effective detectors even when only a small fraction of the dataset has landmark labels. We present results on two toy datasets and four real datasets, with hands and faces, and report new state-of-the-art on two datasets in the wild, e.g. with only 5\% of labeled images we outperform previous state-of-the-art trained on the AFLW dataset.Comment: Published as a conference paper in CVPR 201

    Face Attribute Prediction Using Off-the-Shelf CNN Features

    Full text link
    Predicting attributes from face images in the wild is a challenging computer vision problem. To automatically describe face attributes from face containing images, traditionally one needs to cascade three technical blocks --- face localization, facial descriptor construction, and attribute classification --- in a pipeline. As a typical classification problem, face attribute prediction has been addressed using deep learning. Current state-of-the-art performance was achieved by using two cascaded Convolutional Neural Networks (CNNs), which were specifically trained to learn face localization and attribute description. In this paper, we experiment with an alternative way of employing the power of deep representations from CNNs. Combining with conventional face localization techniques, we use off-the-shelf architectures trained for face recognition to build facial descriptors. Recognizing that the describable face attributes are diverse, our face descriptors are constructed from different levels of the CNNs for different attributes to best facilitate face attribute prediction. Experiments on two large datasets, LFWA and CelebA, show that our approach is entirely comparable to the state-of-the-art. Our findings not only demonstrate an efficient face attribute prediction approach, but also raise an important question: how to leverage the power of off-the-shelf CNN representations for novel tasks.Comment: In proceeding of 2016 International Conference on Biometrics (ICB

    Zero-Shot Recognition using Dual Visual-Semantic Mapping Paths

    Full text link
    Zero-shot recognition aims to accurately recognize objects of unseen classes by using a shared visual-semantic mapping between the image feature space and the semantic embedding space. This mapping is learned on training data of seen classes and is expected to have transfer ability to unseen classes. In this paper, we tackle this problem by exploiting the intrinsic relationship between the semantic space manifold and the transfer ability of visual-semantic mapping. We formalize their connection and cast zero-shot recognition as a joint optimization problem. Motivated by this, we propose a novel framework for zero-shot recognition, which contains dual visual-semantic mapping paths. Our analysis shows this framework can not only apply prior semantic knowledge to infer underlying semantic manifold in the image feature space, but also generate optimized semantic embedding space, which can enhance the transfer ability of the visual-semantic mapping to unseen classes. The proposed method is evaluated for zero-shot recognition on four benchmark datasets, achieving outstanding results.Comment: Accepted as a full paper in IEEE Computer Vision and Pattern Recognition (CVPR) 201
    • …
    corecore