2,605 research outputs found
Winter is here: summarizing Twitter streams related to pre-scheduled events
Pre-scheduled events, such as TV shows and sports games, usually garner considerable attention from the public. Twitter captures large volumes of discussions and messages related to these events, in real-time. Twitter streams related to pre-scheduled events are characterized by the following: (1) spikes in the volume of published tweets reflect the highlights of the event and (2) some of the published tweets make reference to the characters involved in the event, in the context in which they are currently portrayed in a subevent. In this paper, we take advantage of these characteristics to identify the highlights of pre-scheduled events from tweet streams and we demonstrate a method to summarize these highlights. We evaluate our algorithm on tweets collected around 2 episodes of a popular TV show, Game of Thrones, Season 7.Published versio
Second-order Temporal Pooling for Action Recognition
Deep learning models for video-based action recognition usually generate
features for short clips (consisting of a few frames); such clip-level features
are aggregated to video-level representations by computing statistics on these
features. Typically zero-th (max) or the first-order (average) statistics are
used. In this paper, we explore the benefits of using second-order statistics.
Specifically, we propose a novel end-to-end learnable feature aggregation
scheme, dubbed temporal correlation pooling that generates an action descriptor
for a video sequence by capturing the similarities between the temporal
evolution of clip-level CNN features computed across the video. Such a
descriptor, while being computationally cheap, also naturally encodes the
co-activations of multiple CNN features, thereby providing a richer
characterization of actions than their first-order counterparts. We also
propose higher-order extensions of this scheme by computing correlations after
embedding the CNN features in a reproducing kernel Hilbert space. We provide
experiments on benchmark datasets such as HMDB-51 and UCF-101, fine-grained
datasets such as MPII Cooking activities and JHMDB, as well as the recent
Kinetics-600. Our results demonstrate the advantages of higher-order pooling
schemes that when combined with hand-crafted features (as is standard practice)
achieves state-of-the-art accuracy.Comment: Accepted in the International Journal of Computer Vision (IJCV
Framework for Clique-based Fusion of Graph Streams in Multi-function System Testing
The paper describes a framework for multi-function system testing.
Multi-function system testing is considered as fusion (or revelation) of
clique-like structures. The following sets are considered: (i) subsystems
(system parts or units / components / modules), (ii) system functions and a
subset of system components for each system function, and (iii) function
clusters (some groups of system functions which are used jointly). Test
procedures (as units testing) are used for each subsystem. The procedures lead
to an ordinal result (states, colors) for each component, e.g., [1,2,3,4]
(where 1 corresponds to 'out of service', 2 corresponds to 'major faults', 3
corresponds to 'minor faults', 4 corresponds to 'trouble free service'). Thus,
for each system function a graph over corresponding system components is
examined while taking into account ordinal estimates/colors of the components.
Further, an integrated graph (i.e., colored graph) for each function cluster is
considered (this graph integrates the graphs for corresponding system
functions). For the integrated graph (for each function cluster) structure
revelation problems are under examination (revelation of some subgraphs which
can lead to system faults): (1) revelation of clique and quasi-clique (by
vertices at level 1, 2, etc.; by edges/interconnection existence) and (2)
dynamical problems (when vertex colors are functions of time) are studied as
well: existence of a time interval when clique or quasi-clique can exist.
Numerical examples illustrate the approach and problems.Comment: 6 pages, 13 figure
Attentive monitoring of multiple video streams driven by a Bayesian foraging strategy
In this paper we shall consider the problem of deploying attention to subsets
of the video streams for collating the most relevant data and information of
interest related to a given task. We formalize this monitoring problem as a
foraging problem. We propose a probabilistic framework to model observer's
attentive behavior as the behavior of a forager. The forager, moment to moment,
focuses its attention on the most informative stream/camera, detects
interesting objects or activities, or switches to a more profitable stream. The
approach proposed here is suitable to be exploited for multi-stream video
summarization. Meanwhile, it can serve as a preliminary step for more
sophisticated video surveillance, e.g. activity and behavior analysis.
Experimental results achieved on the UCR Videoweb Activities Dataset, a
publicly available dataset, are presented to illustrate the utility of the
proposed technique.Comment: Accepted to IEEE Transactions on Image Processin
An MPEG-7 scheme for semantic content modelling and filtering of digital video
Abstract Part 5 of the MPEG-7 standard specifies Multimedia Description Schemes (MDS); that is, the format multimedia content models should conform to in order to ensure interoperability across multiple platforms and applications. However, the standard does not specify how the content or the associated model may be filtered. This paper proposes an MPEG-7 scheme which can be deployed for digital video content modelling and filtering. The proposed scheme, COSMOS-7, produces rich and multi-faceted semantic content models and supports a content-based filtering approach that only analyses content relating directly to the preferred content requirements of the user. We present details of the scheme, front-end systems used for content modelling and filtering and experiences with a number of users
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