39,351 research outputs found
SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis
Synthesizing realistic images from human drawn sketches is a challenging
problem in computer graphics and vision. Existing approaches either need exact
edge maps, or rely on retrieval of existing photographs. In this work, we
propose a novel Generative Adversarial Network (GAN) approach that synthesizes
plausible images from 50 categories including motorcycles, horses and couches.
We demonstrate a data augmentation technique for sketches which is fully
automatic, and we show that the augmented data is helpful to our task. We
introduce a new network building block suitable for both the generator and
discriminator which improves the information flow by injecting the input image
at multiple scales. Compared to state-of-the-art image translation methods, our
approach generates more realistic images and achieves significantly higher
Inception Scores.Comment: Accepted to CVPR 201
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
Deep Sketch-Photo Face Recognition Assisted by Facial Attributes
In this paper, we present a deep coupled framework to address the problem of
matching sketch image against a gallery of mugshots. Face sketches have the
essential in- formation about the spatial topology and geometric details of
faces while missing some important facial attributes such as ethnicity, hair,
eye, and skin color. We propose a cou- pled deep neural network architecture
which utilizes facial attributes in order to improve the sketch-photo
recognition performance. The proposed Attribute-Assisted Deep Con- volutional
Neural Network (AADCNN) method exploits the facial attributes and leverages the
loss functions from the facial attributes identification and face verification
tasks in order to learn rich discriminative features in a common em- bedding
subspace. The facial attribute identification task increases the inter-personal
variations by pushing apart the embedded features extracted from individuals
with differ- ent facial attributes, while the verification task reduces the
intra-personal variations by pulling together all the fea- tures that are
related to one person. The learned discrim- inative features can be well
generalized to new identities not seen in the training data. The proposed
architecture is able to make full use of the sketch and complementary fa- cial
attribute information to train a deep model compared to the conventional
sketch-photo recognition methods. Exten- sive experiments are performed on
composite (E-PRIP) and semi-forensic (IIIT-D semi-forensic) datasets. The
results show the superiority of our method compared to the state- of-the-art
models in sketch-photo recognition algorithm
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Ideation as an intellectual information acquisition and use context: Investigating game designersâ information-based ideation behavior
Human Information Behavior (HIB) research commonly examines behavior in the context of why information is acquired and how it will be used, but usually at the level of the work or everyday-life tasks the information will support. HIB has not been examined in detail at the broader contextual level of intellectual purpose (i.e. the higher-order conceptual tasks the information was acquired to support). Examination at this level can enhance holistic understanding of HIB as a âmeans to an intellectual endâ and inform the design of digital information environments that support information interaction for specific intellectual purposes. We investigate information-based ideation (IBI) as a specific intellectual information acquisition and use context by conducting Critical Incident-style interviews with ten game designers, focusing on how they interact with information to generate and develop creative design ideas. Our findings give rise to a framework of their ideation-focused HIB, which systems designers can leverage to reason about how best to support certain behaviors to drive design ideation. These findings emphasize the importance of intellectual purpose as a driver for acquisition and desired outcome of use
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