4,269 research outputs found
Regenerative properties of the linear hawkes process with unbounded memory
We prove regenerative properties for the linear Hawkes process under minimal
assumptions on the transfer function, which may have unbounded support. These
results are applicable to sliding window statistical estimators. We exploit
independence in the Poisson cluster point process decomposition, and the
regeneration times are not stopping times for the Hawkes process. The
regeneration time is interpreted as the renewal time at zero of a M/G/infinity
queue, which yields a formula for its Laplace transform. When the transfer
function admits some exponential moments, we stochastically dominate the
cluster length by exponential random variables with parameters expressed in
terms of these moments. This yields explicit bounds on the Laplace transform of
the regeneration time in terms of simple integrals or special functions
yielding an explicit negative upper-bound on its abscissa of convergence. These
regenerative results allow, e.g., to systematically derive long-time asymptotic
results in view of statistical applications. This is illustrated on a
concentration inequality previously obtained with coauthors
Text Recognition Past, Present and Future
Text recognition in various images is a research domain which attempts to develop a computer programs with a feature to read the text from images by the computer. Thus there is a need of character recognition mechanisms which results Document Image Analysis (DIA) which changes different documents in paper format computer generated electronic format. In this paper we have read and analyzed various methods for text recognition from different types of text images like scene images, text images, born digital images and text from videos. Text Recognition is an easy task for people who can read, but to make a computer that does character recognition is highly difficult task. The reasons behind this might be variability, abstraction and absence of various hard-and-fast rules that locate the appearance of a visual character in various text images. Therefore rules that is to be applied need to be very heuristically deduced from samples domain. This paper gives a review for various existing methods. The objective of this paper is to give a summary on well-known methods
Neural Natural Language Processing for Long Texts: A Survey of the State-of-the-Art
The adoption of Deep Neural Networks (DNNs) has greatly benefited Natural
Language Processing (NLP) during the past decade. However, the demands of long
document analysis are quite different from those of shorter texts, while the
ever increasing size of documents uploaded on-line renders automated
understanding of long texts a critical area of research. This article has two
goals: a) it overviews the relevant neural building blocks, thus serving as a
short tutorial, and b) it surveys the state-of-the-art in long document NLP,
mainly focusing on two central tasks: document classification and document
summarization. Sentiment analysis for long texts is also covered, since it is
typically treated as a particular case of document classification.
Additionally, this article discusses the main challenges, issues and current
solutions related to long document NLP. Finally, the relevant, publicly
available, annotated datasets are presented, in order to facilitate further
research.Comment: 53 pages, 2 figures, 171 citation
ON RELEVANCE FILTERING FOR REAL-TIME TWEET SUMMARIZATION
Real-time tweet summarization systems (RTS) require mechanisms for capturing relevant tweets, identifying novel tweets, and capturing timely tweets. In this thesis, we tackle the RTS problem with a main focus on the relevance filtering. We experimented with different traditional retrieval models.
Additionally, we propose two extensions to alleviate the sparsity and topic drift challenges that affect the relevance filtering. For the sparsity, we propose leveraging word embeddings in Vector Space model (VSM) term weighting to empower the system to use semantic similarity alongside the lexical matching. To mitigate the effect of topic drift, we exploit explicit relevance feedback to enhance profile representation to cope with its development in the stream over time.
We conducted extensive experiments over three standard English TREC test collections that were built specifically for RTS. Although the extensions do not generally exhibit better performance, they are comparable to the baselines used.
Moreover, we extended an event detection Arabic tweets test collection, called EveTAR, to support tasks that require novelty in the system's output. We collected novelty judgments using in-house annotators and used the collection to test our RTS system. We report preliminary results on EveTAR using different models of the RTS system.This work was made possible by NPRP grants # NPRP 7-1313-1-245 and # NPRP 7-1330-2-483 from the Qatar National Research Fund (a member of Qatar Foundation)
On utilizing weak estimators to achieve the online classification of data streams
Author's accepted version (post-print).Available from 03/09/2021.acceptedVersio
Audio-visual football video analysis, from structure detection to attention analysis
Sport video is an important video genre. Content-based sports video analysis attracts great interest from both industry and academic fields. A sports video is characterised by repetitive temporal structures, relatively plain contents, and strong spatio-temporal variations, such as quick camera switches and swift local motions. It is necessary to develop specific techniques for content-based sports video analysis to utilise these characteristics.
For an efficient and effective sports video analysis system, there are three fundamental questions: (1) what are key stories for sports videos; (2) what incurs viewer’s interest; and (3) how to identify game highlights. This thesis is developed around these questions. We approached these questions from two different perspectives and in turn three research contributions are presented, namely, replay detection, attack temporal structure decomposition, and attention-based highlight identification.
Replay segments convey the most important contents in sports videos. It is an efficient approach to collect game highlights by detecting replay segments. However, replay is an artefact of editing, which improves with advances in video editing tools. The composition of replay is complex, which includes logo transitions, slow motions, viewpoint switches and normal speed video clips. Since logo transition clips are pervasive in game collections of FIFA World Cup 2002, FIFA World Cup 2006 and UEFA Championship 2006, we take logo transition detection as an effective replacement of replay detection. A two-pass system was developed, including a five-layer adaboost classifier and a logo template matching throughout an entire video. The five-layer adaboost utilises shot duration, average game pitch ratio, average motion, sequential colour histogram and shot frequency between two neighbouring logo transitions, to filter out logo transition candidates. Subsequently, a logo template is constructed and employed to find all transition logo sequences. The precision and recall of this system in replay detection is 100% in a five-game evaluation collection.
An attack structure is a team competition for a score. Hence, this structure is a conceptually fundamental unit of a football video as well as other sports videos. We review the literature of content-based temporal structures, such as play-break structure, and develop a three-step system for automatic attack structure decomposition. Four content-based shot classes, namely, play, focus, replay and break were identified by low level visual features. A four-state hidden Markov model was trained to simulate transition processes among these shot classes. Since attack structures are the longest repetitive temporal unit in a sports video, a suffix tree is proposed to find the longest repetitive substring in the label sequence of shot class transitions. These occurrences of this substring are regarded as a kernel of an attack hidden Markov process. Therefore, the decomposition of attack structure becomes a boundary likelihood comparison between two Markov chains.
Highlights are what attract notice. Attention is a psychological measurement of “notice ”. A brief survey of attention psychological background, attention estimation from vision and auditory, and multiple modality attention fusion is presented. We propose two attention models for sports video analysis, namely, the role-based attention model and the multiresolution autoregressive framework. The role-based attention model is based on the perception structure during watching video. This model removes reflection bias among modality salient signals and combines these signals by reflectors. The multiresolution autoregressive framework (MAR) treats salient signals as a group of smooth random processes, which follow a similar trend but are filled with noise. This framework tries to estimate a noise-less signal from these coarse noisy observations by a multiple resolution analysis. Related algorithms are developed, such as event segmentation on a MAR tree and real time event detection. The experiment shows that these attention-based approach can find goal events at a high precision. Moreover, results of MAR-based highlight detection on the final game of FIFA 2002 and 2006 are highly similar to professionally labelled highlights by BBC and FIFA
WavSpA: Wavelet Space Attention for Boosting Transformers' Long Sequence Learning Ability
Transformer and its variants are fundamental neural architectures in deep
learning. Recent works show that learning attention in the Fourier space can
improve the long sequence learning capability of Transformers. We argue that
wavelet transform shall be a better choice because it captures both position
and frequency information with linear time complexity. Therefore, in this
paper, we systematically study the synergy between wavelet transform and
Transformers. We propose Wavelet Space Attention (WavSpA) that facilitates
attention learning in a learnable wavelet coefficient space which replaces the
attention in Transformers by (1) applying forward wavelet transform to project
the input sequences to multi-resolution bases, (2) conducting attention
learning in the wavelet coefficient space, and (3) reconstructing the
representation in input space via backward wavelet transform. Extensive
experiments on the Long Range Arena demonstrate that learning attention in the
wavelet space using either fixed or adaptive wavelets can consistently improve
Transformer's performance and also significantly outperform learning in Fourier
space. We further show our method can enhance Transformer's reasoning
extrapolation capability over distance on the LEGO chain-of-reasoning task
- …