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A Course In Functional Analysis

The interest in Artificial Intelligence (AI) and its applications has seen unprecedented growth in the last few years. This success can be partly attributed to the advancements made in the sub-fields of AI such as Machine Learning (ML), Computer Vision (CV), and Natural Language Processing (NLP). The largest of the growths in these fields has been made possible with deep learning, a sub-area of machine learning, which uses the principles of artificial neural networks. This has created significant interest in the integration of vision and language. The tasks are designed such that they perfectly embrace the ideas of deep learning. In this survey, we focus on ten prominent tasks that integrate language and vision by discussing their problem formulations, methods, existing datasets, evaluation measures, and compare the results obtained with corresponding state-of-the-art methods. Our efforts go beyond earlier surveys which are either task-specific or concentrate only on one type of visual content, i.e., image or video. Furthermore, we also provide some potential future directions in this field of research with an anticipation that this survey brings in innovative thoughts and ideas to address the existing challenges and build new applications.

Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods

Image captioning has received significant attention with remarkable improvements in recent advances. Nevertheless, images in the wild encapsulate rich knowledge and cannot be sufficiently described with models built on image-caption pairs containing only in-domain objects. In this paper, we propose to address the problem by augmenting standard deep captioning architectures with object learners. Specifically, we present Long Short-Term Memory with Pointing (LSTM-P) --- a new architecture that facilitates vocabulary expansion and produces novel objects via pointing mechanism. Technically, object learners are initially pre-trained on available object recognition data. Pointing in LSTM-P then balances the probability between generating a word through LSTM and copying a word from the recognized objects at each time step in decoder stage. Furthermore, our captioning encourages global coverage of objects in the sentence. Extensive experiments are conducted on both held-out COCO image captioning and ImageNet datasets for describing novel objects, and superior results are reported when comparing to state-of-the-art approaches. More remarkably, we obtain an average of 60.9% in F1 score on held-out COCO dataset.

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Pointing Novel Objects in Image Captioning

Image-text matching is a vital cross-modality task in artificial intelligence and has attracted increasing attention in recent years. Existing works have shown that learning semantic concepts is useful to enhance image representation and can significantly improve the performance of both image-to-text and text-to-image retrieval. However, existing models simply detect semantic concepts from a given image, which are less likely to deal with long-tail and occlusion concepts. Frequently co-occurred concepts in the same scene, e.g. bedroom and bed, can provide common-sense knowledge to discover other semantic-related concepts. In this paper, we develop a Scene Concept Graph (SCG) by aggregating image scene graphs and extracting frequently co-occurred concept pairs as scene common-sense knowledge. Moreover, we propose a novel model to incorporate this knowledge to improve image-text matching. Specifically, semantic concepts are detected from images and then expanded by the SCG. After learning to select relevant contextual concepts, we fuse their representations with the image embedding feature to feed into the matching module. Extensive experiments are conducted on Flickr30K and MSCOCO datasets, and prove that our model achieves state-of-the-art results due to the effectiveness of incorporating the external SCG.

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Knowledge Aware Semantic Concept Expansion for Image-Text Matching

System log clustering approaches for cyber security applications: A survey

Mining patterns from the log messages is valuable for real-time analysis and detecting faults, anomaly and security threats. A data-streaming algorithm with an efficient pattern finding approach is more practical way to classify these ubiquitous logs. Thus, in this paper the authors propose a novel online approach for finding patterns in log data sets where a locally sensitive signature is generated for similar log messages. The similarity of these log messages is identified by parsing log messages and then, logically analyzing the signature bit stream associated with them. In addition to that the approach is intelligent enough to reflect the changes when a totally new log appears in the system. The validation of the proposed method is done by comparing F-measure of clustering results for labeled datasets and the word order matched percentage of the log messages in a cluster for unlabeled case with that of SLCT.

Intelligent Clustering Scheme for Log Data Streams

Images in the wild encapsulate rich knowledge about varied abstract concepts and cannot be sufficiently described with models built only using image-caption pairs containing selected objects. We propose to handle such a task with the guidance of a knowledge base that incorporate many abstract concepts. Our method is a two-step process where we first build a multi-entity-label image recognition model to predict abstract concepts as image labels and then leverage them in the second step as an external semantic attention and constrained inference in the caption generation model for describing images that depict unseen/novel objects. Evaluations show that our models outperform most of the prior work for out-of-domain captioning on MSCOCO and are useful for integration of knowledge and vision in general.

Describing Natural Images Containing Novel Objects with Knowledge Guided Assitance

This paper considers extractive summarisation in a comparative setting: given two or more document groups (e.g., separated by publication time), the goal is to select a small number of documents that are representative of each group, and also maximally distinguishable from other groups. We formulate a set of new objective functions for this problem that connect recent literature on document summarisation, interpretable machine learning, and data subset selection. In particular, by casting the problem as a binary classification amongst different groups, we derive objectives based on the notion of maximum mean discrepancy, as well as a simple yet effective gradient-based optimisation strategy. Our new formulation allows scalable evaluations of comparative summarisation as a classification task, both automatically and via crowd-sourcing. To this end, we evaluate comparative summarisation methods on a newly curated collection of controversial news topics over 13 months. We observe that gradient-based optimisation outperforms discrete and baseline approaches in 14 out of 24 different automatic evaluation settings. In crowd-sourced evaluations, summaries from gradient optimisation elicit 7% more accurate classification from human workers than discrete optimisation. Our result contrasts with recent literature on submodular data subset selection that favours discrete optimisation. We posit that our formulation of comparative summarisation will prove useful in a diverse range of use cases such as comparing content sources, authors, related topics, or distinct view points.

/pdf/comparative-document-summarisation-via-classification-17tzw7xhny.pdf

Comparative Document Summarisation via Classification

Thispaperconsidersextractivesummarisationinacomparative setting: given two or more document groups (e.g., separated by publication time), the goal is to select a small number of documents that are representative of each group, and also maximally distinguishable from other groups. We formulate a set of new objective functions for this problem that connect recent literature on document summarisation, interpretable machine learning, and data subset selection. In particular, by casting the problem as a binary classification amongst different groups, we derive objectives based on the notion of maximum mean discrepancy, as well as a simple yet effective gradient-based optimisation strategy. Our new formulation allows scalable evaluations of comparative summarisation as a classification task, both automatically and via crowd-sourcing. To this end, we evaluate comparative summarisation methods on a newly curated collection of controversial news topics over 13months.Weobserve thatgradient-based optimisationoutperforms discrete and baseline approaches in 15 out of 24 different automatic evaluation settings. In crowd-sourced evaluations, summaries from gradient optimisation elicit 7% more accurate classification from human workers than discrete optimisation. Our result contrasts with recent literature on submodular data subset selection that favours discrete optimisation. We posit that our formulation of comparative summarisation will prove useful in a diverse range of use cases such as comparing content sources, authors, related topics, or distinct view points.

/pdf/comparative-document-summarisation-via-classification-2j7kmdfxwj.pdf

Images on the Web encapsulate diverse knowledge about varied abstract concepts. They cannot be sufficiently described with models learned from image-caption pairs that mention only a small number of visual object categories. In contrast, large-scale knowledge graphs contain many more concepts that can be detected by image recognition models. Hence, to assist description generation for those images which contain visual objects unseen in image-caption pairs, we propose a two-step process by leveraging large-scale knowledge graphs. In the first step, a multi-entity recognition model is built to annotate images with concepts not mentioned in any caption. In the second step, those annotations are leveraged as external semantic attention and constrained inference in the image description generation model. Evaluations show that our models outperform most of the prior work on out-of-domain MSCOCO image description generation and also scales better to broad domains with more unseen objects.

Umanga Bista

Papers

Intelligent Clustering Scheme for Log Data Streams

Describing Natural Images Containing Novel Objects with Knowledge Guided Assitance

Comparative Document Summarisation via Classification

Comparative Document Summarisation via Classification

Knowledge Guided Attention and Inference for Describing Images Containing Unseen Objects