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Full-Text Articles in Engineering
Show, Prefer And Tell: Incorporating User Preferences Into Image Captioning, Annika Lindh, Robert J. Ross, John Kelleher
Show, Prefer And Tell: Incorporating User Preferences Into Image Captioning, Annika Lindh, Robert J. Ross, John Kelleher
Conference papers
Image Captioning (IC) is the task of generating natural language descriptions for images. Models encode the image using a convolutional neural network (CNN) and generate the caption via a recurrent model or a multi-modal transformer. Success is measured by the similarity between generated captions and human-written “ground-truth” captions, using the CIDEr [14], SPICE [1] and METEOR [2] metrics. While incremental gains have been made on these metrics, there is a lack of focus on end-user opinions on the amount of content in captions. Studies with blind and low-vision participants have found that lack of detail is a problem [6, 13, …
Reality Analagous Synthetic Dataset Generation With Daylight Variance For Deep Learning Classification, Thomas Lee, Susan Mckeever, Jane Courtney
Reality Analagous Synthetic Dataset Generation With Daylight Variance For Deep Learning Classification, Thomas Lee, Susan Mckeever, Jane Courtney
Conference papers
For the implementation of Autonomously navigating Unmanned Air Vehicles (UAV) in the real world, it must be shown that safe navigation is possible in all real world scenarios. In the case of UAVs powered by Deep Learning algorithms, this is a difficult task to achieve, as the weak point of any trained network is the reduction in predictive capacity when presented with unfamiliar input data. It is possible to train for more use cases, however more data is required for this, requiring time and manpower to acquire. In this work, a potential solution to the manpower issues of exponentially scaling …
Modern Techniques For Discovering Digital Steganography, Michael Hegarty, Anthony Keane
Modern Techniques For Discovering Digital Steganography, Michael Hegarty, Anthony Keane
Conference papers
Digital steganography can be difficult to detect and as such is an ideal way of engaging in covert communications across the Internet. This research paper is a work-in-progress report on instances of steganography that were identified on websites on the Internet including some from the DarkWeb using the application of new methods of deep learning algorithms. This approach to the identification of Least Significant Bit (LSB) Steganography using Convolutional Neural Networks (CNN) has demonstrated some efficiency for image classification. The CNN algorithm was trained using datasets of images with known steganography and then applied to datasets with images to identify …
Mediaeval2019: Flood Detection In Time Sequence Satellite Images, Palavi Jain, Bianca Schoen-Phelan, Robert J. Ross
Mediaeval2019: Flood Detection In Time Sequence Satellite Images, Palavi Jain, Bianca Schoen-Phelan, Robert J. Ross
Conference papers
In this work, we present a flood detection technique from time series satellite images for the City-centered satellite sequences (CCSS) task in the MediaEval 2019 competition [1]. This work utilises a three channel feature indexing technique [13] along with a VGG16 pretrained model for automatic detection of floods. We also compared our result with RGB images and a modified NDWI technique by Mishra et al, 2015 [15]. The result shows that the three channel feature indexing technique performed the best with VGG16 and is a promising approach to detect floods from time series satellite images.
A Multi-Task Approach To Incremental Dialogue State Tracking, Anh Duong Trinh, Robert J. Ross, John D. Kelleher
A Multi-Task Approach To Incremental Dialogue State Tracking, Anh Duong Trinh, Robert J. Ross, John D. Kelleher
Conference papers
Incrementality is a fundamental feature of language in real world use. To this point, however, the vast majority of work in automated dialogue processing has focused on language as turn based. In this paper we explore the challenge of incremental dialogue state tracking through the development and analysis of a multi-task approach to incremental dialogue state tracking. We present the design of our incremental dialogue state tracker in detail and provide evaluation against the well known Dialogue State Tracking Challenge 2 (DSTC2) dataset. In addition to a standard evaluation of the tracker, we also provide an analysis of the Incrementality …
Investigating The Impact Of Unsupervised Feature-Extraction From Multi-Wavelength Image Data For Photometric Classification Of Stars, Galaxies And Qsos, Annika Lindh
Conference papers
Accurate classification of astronomical objects currently relies on spectroscopic data. Acquiring this data is time-consuming and expensive compared to photometric data. Hence, improving the accuracy of photometric classification could lead to far better coverage and faster classification pipelines. This paper investigates the benefit of using unsupervised feature-extraction from multi-wavelength image data for photometric classification of stars, galaxies and QSOs. An unsupervised Deep Belief Network is used, giving the model a higher level of interpretability thanks to its generative nature and layer-wise training. A Random Forest classifier is used to measure the contribution of the novel features compared to a set …