http://www.xrce.xerox.com/ en-GB ezp@xrce.xerox.com () Tue, 05 Sep 2017 10:30:47 +0000 Tue, 05 Sep 2017 10:30:47 +0000 eZ Components Feed dev (http://ezcomponents.org/docs/tutorials/Feed) http://www.rssboard.org/rss-specification http://www.xrce.xerox.com/Our-Research/Publications/2017-177-Multi-Context-Systems-for-Consistency-Validation-and-Querying-of-Business-Process-Models Large organizations today face a growing challenge of managing heterogeneous process collections containing business processes. Explicit semantics inherent to domain-specific models can help alleviate some of the management challenges. Starting with concept definitions, designers can create domain specific processes and eventually generate industry-standard BPMN for use in BPMS solutions. However, in such a multi-layered setting, any of these artefacts (concepts, domain processes and BPMN) can be modified by various stakeholders and changes done by one person may influence models used by others. There is therefore a need for tool support to aid in keeping track of changes done and their impacts on different stakeholders. In this paper, we present a multi-context systems based approach that allows inferring impacts of changes, especially in terms of consistency, and executing semantic queries. In contrast to existing work, our framework allows the co-existence of different formalisms, with potentially different characteristics, offering greater flexibility in knowledge base and tool integration. ezp@xrce.xerox.com (Administrator User) xerox_db_publication_77 Mon, 22 May 2017 12:55:02 +0000 http://www.xrce.xerox.com/Our-Research/Publications/2017-136-Experimenting-Word-Embeddings-in-Assisting-Legal-Review As advanced technologies such as data mining become part of the everyday workflow of document reviews in litigations, keyword-search still appears to serve as a cornerstone approach in responsive (or privilege) review. Keywords are conceptually easy to understand and help culling documents at the early stages of the review. But developing proper keywords to minimize the risk of under/overinclusiveness can lead to complex strategies. To cope with the burden of designing search terms, we propose to use semantic search in a dynamic manner. This paper describes a system leveraging semantic models in a smart review environment in order to support knowledge workers in eDiscovery. ezp@xrce.xerox.com (Administrator User) xerox_db_publication_36 Thu, 18 May 2017 12:17:01 +0000 http://www.xrce.xerox.com/Our-Research/Publications/2017-191-Dialog-state-tracking.-a-machine-reading-approach-using-Memory-Networks Machine reading using differentiable reasoning models has recently shown remarkable progress. In this context, End-to-End trainable Memory Networks (MemN2N) have demonstrated promising performance on simple natural language based reasoning tasks such as factual reasoning and basic deduction. However, other tasks, namely multi-fact question-answering, positional reasoning or dialog related tasks, remain challenging particularly due to the necessity of more complex interactions between the memory and controller modules composing this family of models. In this paper, we introduce a novel end-to-end memory access regulation mechanism inspired by the current progress on the connection short-cutting principle in the field of computer vision. Concretely, we develop a Gated End-to-End trainable Memory Network architecture (GMemN2N). From the machine learning perspective, this new capability is learned in an end-to-end fashion without the use of any additional supervision signal which is, as far as our knowledge goes, the first of its kind. Our experiments show significant improvements on the most challenging tasks in the 20 bAbI dataset, without the use of any domain knowledge. Then, we show improvements on the Dialog bAbI tasks including the real human-bot conversion-based Dialog State Tracking Challenge (DSTC-2) dataset. On these two datasets, our model sets the new state of the art. ezp@xrce.xerox.com (Administrator User) xerox_db_publication_91 Fri, 05 May 2017 09:24:01 +0000 http://www.xrce.xerox.com/Our-Research/Publications/2017-190-Gated-End-to-End-Memory-Networks Machine reading using differentiable reasoning models has recently shown remarkable progress. In this context, End-to-End trainable Memory Networks (MemN2N) have demonstrated promising performance on simple natural language based reasoning tasks such as factual reasoning and basic deduction. However, other tasks, namely multi-fact question-answering, positional reasoning or dialog related tasks, remain challenging particularly due to the necessity of more complex interactions between the memory and controller modules composing this family of models. In this paper, we introduce a novel end-to-end memory access regulation mechanism inspired by the current progress on the connection short-cutting principle in the field of computer vision. Concretely, we develop a Gated End-to-End trainable Memory Network architecture (GMemN2N). From the machine learning perspective, this new capability is learned in an end-to-end fashion without the use of any additional supervision signal which is, as far as our knowledge goes, the first of its kind. Our experiments show significant improvements on the most challenging tasks in the 20 bAbI dataset, without the use of any domain knowledge. Then, we show improvements on the Dialog bAbI tasks including the real human-bot conversion-based Dialog State Tracking Challenge (DSTC-2) dataset. On these two datasets, our model sets the new state of the art. ezp@xrce.xerox.com (Administrator User) xerox_db_publication_90 Fri, 05 May 2017 09:23:02 +0000 http://www.xrce.xerox.com/Our-Research/Publications/2016-121-AnchorNet-A-Weakly-Supervised-Network-to-Learn-Geometry-sensitive-Features-For-Semantic-Matching Despite significant progress of deep learning in recent years, state-of-the-art semantic matching methods still rely on legacy features such as SIFT or HoG. We argue that the strong invariance properties that are key to the success of recent deep architectures on the classification task make them unfit for dense correspondence tasks, unless a large amount of supervision is used. In this work, we propose a deep network, termed AnchorNet, that produces image representations that are well-suited for semantic matching. It relies on a set of filters whose response is geometrically consistent across different object instances, even in the presence of strong intra-class, scale, or viewpoint variations. Trained only with weak image-level labels, the final representation successfully captures information about the object structure and improves results of state-of-the-art semantic matching methods such as the deformable spatial pyramid or the proposal flow methods. We show positive results on the cross-instance matching task where different instances of the same object category are matched as well as on a new cross-category semantic matching task aligning pairs of instances each from a different object class. ezp@xrce.xerox.com (Administrator User) xerox_db_publication_21 Thu, 13 Apr 2017 07:38:01 +0000 http://www.xrce.xerox.com/Our-Research/Publications/2017-183-LCR-Net-Localization-Classification-Regression-for-Human-Pose We propose an end-to-end architecture for joint 2D and 3D human pose estimation in natural images. Key to our approach is the generation and scoring of a number of pose proposals per image, which allows us to predict 2D and 3D pose of multiple people simultaneously. Hence, our approach does not require an approximate localization of the humans for initialization. Our architecture, named LCR-Net, contains 3 main components: 1) the pose proposal generator that suggests potential poses at different locations in the image; 2) a classifier that scores the different pose proposals ; and 3) a regressor that refines pose proposals both in 2D and 3D. All three stages share the convolutional feature layers and are trained jointly. The final pose estimation is obtained by integrating over neighboring pose hypotheses , which is shown to improve over a standard non maximum suppression algorithm. Our approach significantly outperforms the state of the art in 3D pose estimation on Human3.6M, a controlled environment. Moreover, it shows promising results on real images for both single and multi-person subsets of the MPII 2D pose benchmark. ezp@xrce.xerox.com (Administrator User) xerox_db_publication_83 Wed, 12 Apr 2017 12:05:02 +0000 http://www.xrce.xerox.com/Our-Research/Publications/2016-122-Beyond-instance-level-image-retrieval-Leveraging-human-captions-to-learn-representations-for-semantic-visual-search Querying a database using an example image has been is a simple and intuitive interface to retrieve information in a database of images. Consequently instance-level image retrieval has been heavily studied in the computer vision community in the last decade. One problem that has been overlooked though is the retrieval of similar visual scenes, where the retrieved images do not exhibit the same object instance as the query image, but the images share the same semantic. In this paper, we define the task of semantic image retrieval, and show through a user study that, despite its subjective nature, it is consistently implemented across a pool of human annotators. Our study also shows that region-level captions constitute a good proxy to semantic similarity. Following this observation, we leverage human captions to learning a global image representation that is compact and still performs well at the semantic retrieval task. We also show that we can jointly train visual and textual embeddings that allow to query with both image and text (although the database has no textual annotation) and to perform arithmetic operations on this joint embedding. As a by-product of the learning, the network can be used to visualize which regions contributed the most to the similarity between two images, allowing to interpret our semantic retrieval results in an visual and intuitive way. ezp@xrce.xerox.com (Administrator User) xerox_db_publication_22 Wed, 05 Apr 2017 11:56:02 +0000 http://www.xrce.xerox.com/Our-Research/Publications/2016-123-Procedural-Generation-of-Videos-to-Train-Deep-Action-Recognition-Networks Deep learning for human action recognition in videos is making significant progress, but is slowed down by its dependency on expensive manual labeling of large video collections. In this work, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for "Procedural Human Action Videos". It contains a total of 39,982 videos, with more than 1,000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We introduce a deep multi-task representation learning architecture to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF101 and HMDB51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, significantly outperforming fine-tuning state-of-the-art unsupervised generative models of videos. ezp@xrce.xerox.com (Administrator User) xerox_db_publication_23 Tue, 04 Apr 2017 15:57:02 +0000 http://www.xrce.xerox.com/Our-Research/Publications/2016-102-Dependency-Modelling-for-Inconsistency-Management-in-Digital-Preservation-The-PERICLES-Approach3 The rise of the Semantic Web has provided cultural heritage researchers and prac-titioners with several tools for providing semantically rich representations and in-teroperability of cultural heritage collections. Although indeed offering a lot of advantages, these tools, which come mostly in the form of ontologies and related vocabularies, do not provide a conceptual model for capturing contextual and en-vironmental dependencies, contributing to long-term digital preservation. This paper presents one of the key outcomes of the PERICLES FP7 project, the Linked Resource Model, for modelling dependencies as a set of evolving linked resources. The adoption of the proposed model and the consistency of its repre-sentation are evaluated via a specific instantiation involving the domain of digital video art. ezp@xrce.xerox.com (Administrator User) xerox_db_publication_2 Mon, 03 Apr 2017 09:40:04 +0000 http://www.xrce.xerox.com/Our-Research/Publications/2017-138-What-Can-I-Do-Now.-Guiding-Users-in-an-World-of-Automated-Decisions More and more processes governing our lives use in some part an automatic decision step, where – based on a feature vector derived from an applicant -- an algorithm has the decision power over the final outcome. Here we present a simple idea which gives some of the power back to the applicant by providing her with alternatives which would make the decision algorithm decide differently. It is based on a formalization reminiscent of methods used for active learning and adversarial learning. This has been implemented for the specific case of decision forests (ensemble methods based on decision trees), mapping the problem to an iterative version of enumerating k-cliques. ezp@xrce.xerox.com (Administrator User) xerox_db_publication_38 Wed, 15 Mar 2017 16:56:02 +0000