45,550 research outputs found
Learning to Discriminate Information for Online Action Detection
From a streaming video, online action detection aims to identify actions in
the present. For this task, previous methods use recurrent networks to model
the temporal sequence of current action frames. However, these methods overlook
the fact that an input image sequence includes background and irrelevant
actions as well as the action of interest. For online action detection, in this
paper, we propose a novel recurrent unit to explicitly discriminate the
information relevant to an ongoing action from others. Our unit, named
Information Discrimination Unit (IDU), decides whether to accumulate input
information based on its relevance to the current action. This enables our
recurrent network with IDU to learn a more discriminative representation for
identifying ongoing actions. In experiments on two benchmark datasets, TVSeries
and THUMOS-14, the proposed method outperforms state-of-the-art methods by a
significant margin. Moreover, we demonstrate the effectiveness of our recurrent
unit by conducting comprehensive ablation studies.Comment: To appear in CVPR 202
Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling
Spambot detection in online social networks is a long-lasting challenge
involving the study and design of detection techniques capable of efficiently
identifying ever-evolving spammers. Recently, a new wave of social spambots has
emerged, with advanced human-like characteristics that allow them to go
undetected even by current state-of-the-art algorithms. In this paper, we show
that efficient spambots detection can be achieved via an in-depth analysis of
their collective behaviors exploiting the digital DNA technique for modeling
the behaviors of social network users. Inspired by its biological counterpart,
in the digital DNA representation the behavioral lifetime of a digital account
is encoded in a sequence of characters. Then, we define a similarity measure
for such digital DNA sequences. We build upon digital DNA and the similarity
between groups of users to characterize both genuine accounts and spambots.
Leveraging such characterization, we design the Social Fingerprinting
technique, which is able to discriminate among spambots and genuine accounts in
both a supervised and an unsupervised fashion. We finally evaluate the
effectiveness of Social Fingerprinting and we compare it with three
state-of-the-art detection algorithms. Among the peculiarities of our approach
is the possibility to apply off-the-shelf DNA analysis techniques to study
online users behaviors and to efficiently rely on a limited number of
lightweight account characteristics
Am I Done? Predicting Action Progress in Videos
In this paper we deal with the problem of predicting action progress in
videos. We argue that this is an extremely important task since it can be
valuable for a wide range of interaction applications. To this end we introduce
a novel approach, named ProgressNet, capable of predicting when an action takes
place in a video, where it is located within the frames, and how far it has
progressed during its execution. To provide a general definition of action
progress, we ground our work in the linguistics literature, borrowing terms and
concepts to understand which actions can be the subject of progress estimation.
As a result, we define a categorization of actions and their phases. Motivated
by the recent success obtained from the interaction of Convolutional and
Recurrent Neural Networks, our model is based on a combination of the Faster
R-CNN framework, to make frame-wise predictions, and LSTM networks, to estimate
action progress through time. After introducing two evaluation protocols for
the task at hand, we demonstrate the capability of our model to effectively
predict action progress on the UCF-101 and J-HMDB datasets
Linear-time Online Action Detection From 3D Skeletal Data Using Bags of Gesturelets
Sliding window is one direct way to extend a successful recognition system to
handle the more challenging detection problem. While action recognition decides
only whether or not an action is present in a pre-segmented video sequence,
action detection identifies the time interval where the action occurred in an
unsegmented video stream. Sliding window approaches for action detection can
however be slow as they maximize a classifier score over all possible
sub-intervals. Even though new schemes utilize dynamic programming to speed up
the search for the optimal sub-interval, they require offline processing on the
whole video sequence. In this paper, we propose a novel approach for online
action detection based on 3D skeleton sequences extracted from depth data. It
identifies the sub-interval with the maximum classifier score in linear time.
Furthermore, it is invariant to temporal scale variations and is suitable for
real-time applications with low latency
Transfer Effect of Speech-sound Learning on Auditory-motor Processing of Perceived Vocal Pitch Errors
Speech perception and production are intimately linked. There is evidence that speech motor learning results in changes to auditory processing of speech. Whether speech motor control benefits from perceptual learning in speech, however, remains unclear. This event-related potential study investigated whether speech-sound learning can modulate the processing of feedback errors during vocal pitch regulation. Mandarin speakers were trained to perceive five Thai lexical tones while learning to associate pictures with spoken words over 5 days. Before and after training, participants produced sustained vowel sounds while they heard their vocal pitch feedback unexpectedly perturbed. As compared to the pre-training session, the magnitude of vocal compensation significantly decreased for the control group, but remained consistent for the trained group at the post-training session. However, the trained group had smaller and faster N1 responses to pitch perturbations and exhibited enhanced P2 responses that correlated significantly with their learning performance. These findings indicate that the cortical processing of vocal pitch regulation can be shaped by learning new speech-sound associations, suggesting that perceptual learning in speech can produce transfer effects to facilitating the neural mechanisms underlying the online monitoring of auditory feedback regarding vocal production
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