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Analyzing spatio-temporal data like video is a challenging task that requires processing visual and temporal information effectively. Convolutional Neural Networks have shown promise as baseline fixed feature extractors through transfer learning, a technique that helps minimize the training cost on visual information. Temporal information is often handled using hand-crafted features or Recurrent Neural...
Due both to the speed and quality of their sensors and restrictive on-board computational capabilities, current state-of-the-art (SOA) size, weight, and power (SWaP) constrained autonomous robotic systems are limited in their abilities to sample, fuse, and analyze sensory data for state estimation. Aimed at improving SWaP-constrained robotic state estimation, we present Multi-Hypothesis DeepEfference...
Models are perceived as effective tools for stakeholder communication and analysis concerning a given system. Enterprise Architecture assists organizational change with the creation and maintenance of models as a means to mitigate the business-IT misalignment and supporting decision-making regarding the strategic vision of the enterprise. Literature has acknowledged the topic of enterprise architecture...
We evaluated the support proposed by the RSO to represent graphically our EAM-ISSRM (Enterprise Architecture Management - Information System Security Risk Management) integrated model. The evaluation of the RSO visual notation has been done at two different levels: completeness with regards to the EAM-ISSRM integrated model (Section III) and cognitive effectiveness, relying on the nine principles...
For large-scale visual search, highly compressed yet meaningful representations of images are essential. Structured vector quantizers based on product quantization and its variants are usually employed to achieve such compression while minimizing the loss of accuracy. Yet, unlike binary hashing schemes, these unsupervised methods have not yet benefited from the supervision, end-to-end learning and...
We propose a technique for producing ‘visual explanations’ for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them more transparent. Our approach – Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept (say logits for ‘dog’ or even a caption), flowing into the final convolutional layer to produce a coarse localization...
The contribution of this paper is to bridge the gap on understanding the mathematical structure and the computational implementation of a convolutional neural network using a minimal model. The proposed minimal convolutional neural network is presented using a layering approach. This approach provides a clear understanding of the main mathematical operations in a convolutional neural network. Hence,...
Human sketches are unique in being able to capture both the spatial topology of a visual object, as well as its subtle appearance details. Fine-grained sketch-based image retrieval (FG-SBIR) importantly leverages on such fine-grained characteristics of sketches to conduct instance-level retrieval of photos. Nevertheless, human sketches are often highly abstract and iconic, resulting in severe misalignments...
By stacking layers of convolution and nonlinearity, convolutional networks (ConvNets) effectively learn from lowlevel to high-level features and discriminative representations. Since the end goal of large-scale recognition is to delineate complex boundaries of thousands of classes, adequate exploration of feature distributions is important for realizing full potentials of ConvNets. However, state-of-theart...
During the last years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in image classification. Their architectures have largely drawn inspiration by models of the primate visual system. However, while recent research results of neuroscience prove the existence of non-linear operations in the response of complex visual cells, little effort has been devoted to extend...
In this work we propose a novel framework named Dual-Net aiming at learning more accurate representation for image recognition. Here two parallel neural networks are coordinated to learn complementary features and thus a wider network is constructed. Specifically, we logically divide an end-to-end deep convolutional neural network into two functional parts, i.e., feature extractor and image classifier...
Person Re-identification (re-id) aims to match people across non-overlapping camera views in a public space. It is a challenging problem because many people captured in surveillance videos wear similar clothes. Consequently, the differences in their appearance are often subtle and only detectable at the right location and scales. Existing re-id models, particularly the recently proposed deep learning...
This paper presents a new approach for identifying unknown and/or unwanted states within a system of systems (SoS) architecture using a graphical representation of the event-based modeling language, Monterey Phoenix. The paper demonstrates how the graphical modeling tool can create a single model that contains a mix of human, system, and environmental events, all of which contain event attributes...
A system that dynamically self-adapts at runtime, should comply with critical requirements. However, runtime verification is difficult even when the system was originally formulated to expect adaptation and allowable changes are preconfigured or prespecified. Our approach examines verification processes originally performed for compliance with system requirements to identify specific verification...
The development of a deep (stacked) convolutional auto-encoder in the Caffe deep learning framework is presented in this paper. We describe simple principles which we used to create this model in Caffe. The proposed model of convolutional auto-encoder does not have pooling/unpooling layers yet. The results of our experimental research show comparable accuracy of dimensionality reduction in comparison...
Software visualizations provide many different complex views with different filters and metrics. But often users have a specific question to which they want to have an answer or they need to find the best visualization by themselves and are not aware of other metrics and possibilities of the visualization tool. We propose an interaction with software visualizations based on a conversational interface...
Area V5 or Middle Temporal (MT) area of the primate brain is said to be involved in visual motion perception. Physiological studies indicate that the neurons in MT respond selectively to the direction of moving stimuli. However in response to the complex stimuli containing multiple oriented components, a set of MT neurons are selective to the direction of the component motion whereas the other set...
In this paper we propose a neural network allowing a mobile robot to learn artwork appreciation. The learning is based on the social referencing approach. The robot acquires its knowledge (artificial taste) from the interaction with humans. We present and analyze specifically the visual system, its impact on the robot behavior, and at the end, we analyze the readability of our robot behavior according...
The unified program architecture of real time vision system (VS) with several fields of view construction is described. The offered architecture provides both onboard and stationary usage. In case of stationary usage, it provides determination of location and trajectory of objects movement in the ordered system of co-ordinates. Principles of open architecture, componential technology and use of standard...
The use of Recurrent Neural Networks for video captioning has recently gained a lot of attention, since they can be used both to encode the input video and to generate the corresponding description. In this paper, we present a recurrent video encoding scheme which can discover and leverage the hierarchical structure of the video. Unlike the classical encoder-decoder approach, in which a video is encoded...
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