128,709 research outputs found
Person Recognition in Personal Photo Collections
Recognising persons in everyday photos presents major challenges (occluded
faces, different clothing, locations, etc.) for machine vision. We propose a
convnet based person recognition system on which we provide an in-depth
analysis of informativeness of different body cues, impact of training data,
and the common failure modes of the system. In addition, we discuss the
limitations of existing benchmarks and propose more challenging ones. Our
method is simple and is built on open source and open data, yet it improves the
state of the art results on a large dataset of social media photos (PIPA).Comment: Accepted to ICCV 2015, revise
Perfect Snap
Taking group photos during important events is a common practice. Group photos are taken to remember cheerful times when people had an opportunity to meet many other people. However, an unappealing facial expression of one person can easily ruin the entire photo. Capturing the wrong moments when a person doesnât look attractive can leave him/ her displeased from the complete event experience. A solution is to develop a mobile app that captures the moment when everyone is smiling with eyes wide open. Our solution aims to develop an iPhone app that will preclude users from worrying about not having a great group picture
User profiles matching for different social networks based on faces embeddings
It is common practice nowadays to use multiple social networks for different
social roles. Although this, these networks assume differences in content type,
communications and style of speech. If we intend to understand human behaviour
as a key-feature for recommender systems, banking risk assessments or
sociological researches, this is better to achieve using a combination of the
data from different social media. In this paper, we propose a new approach for
user profiles matching across social media based on embeddings of publicly
available users' face photos and conduct an experimental study of its
efficiency. Our approach is stable to changes in content and style for certain
social media.Comment: Submitted to HAIS 2019 conferenc
Adversarial Image Perturbation for Privacy Protection â A Game Theory Perspective
Users like sharing personal photos with others through social media. At the
same time, they might want to make automatic identification in such photos
difficult or even impossible. Classic obfuscation methods such as blurring are
not only unpleasant but also not as effective as one would expect. Recent
studies on adversarial image perturbations (AIP) suggest that it is possible to
confuse recognition systems effectively without unpleasant artifacts. However,
in the presence of counter measures against AIPs, it is unclear how effective
AIP would be in particular when the choice of counter measure is unknown. Game
theory provides tools for studying the interaction between agents with
uncertainties in the strategies. We introduce a general game theoretical
framework for the user-recogniser dynamics, and present a case study that
involves current state of the art AIP and person recognition techniques. We
derive the optimal strategy for the user that assures an upper bound on the
recognition rate independent of the recogniser's counter measure. Code is
available at https://goo.gl/hgvbNK.Comment: To appear at ICCV'1
Automatic Synchronization of Multi-User Photo Galleries
In this paper we address the issue of photo galleries synchronization, where
pictures related to the same event are collected by different users. Existing
solutions to address the problem are usually based on unrealistic assumptions,
like time consistency across photo galleries, and often heavily rely on
heuristics, limiting therefore the applicability to real-world scenarios. We
propose a solution that achieves better generalization performance for the
synchronization task compared to the available literature. The method is
characterized by three stages: at first, deep convolutional neural network
features are used to assess the visual similarity among the photos; then, pairs
of similar photos are detected across different galleries and used to construct
a graph; eventually, a probabilistic graphical model is used to estimate the
temporal offset of each pair of galleries, by traversing the minimum spanning
tree extracted from this graph. The experimental evaluation is conducted on
four publicly available datasets covering different types of events,
demonstrating the strength of our proposed method. A thorough discussion of the
obtained results is provided for a critical assessment of the quality in
synchronization.Comment: ACCEPTED to IEEE Transactions on Multimedi
Exploiting multimedia in creating and analysing multimedia Web archives
The data contained on the web and the social web are inherently multimedia and consist of a mixture of textual, visual and audio modalities. Community memories embodied on the web and social web contain a rich mixture of data from these modalities. In many ways, the web is the greatest resource ever created by human-kind. However, due to the dynamic and distributed nature of the web, its content changes, appears and disappears on a daily basis. Web archiving provides a way of capturing snapshots of (parts of) the web for preservation and future analysis. This paper provides an overview of techniques we have developed within the context of the EU funded ARCOMEM (ARchiving COmmunity MEMories) project to allow multimedia web content to be leveraged during the archival process and for post-archival analysis. Through a set of use cases, we explore several practical applications of multimedia analytics within the realm of web archiving, web archive analysis and multimedia data on the web in general
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