3,182 research outputs found
Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning
Visual aesthetic assessment has been an active research field for decades.
Although latest methods have achieved promising performance on benchmark
datasets, they typically rely on a large number of manual annotations including
both aesthetic labels and related image attributes. In this paper, we revisit
the problem of image aesthetic assessment from the self-supervised feature
learning perspective. Our motivation is that a suitable feature representation
for image aesthetic assessment should be able to distinguish different
expert-designed image manipulations, which have close relationships with
negative aesthetic effects. To this end, we design two novel pretext tasks to
identify the types and parameters of editing operations applied to synthetic
instances. The features from our pretext tasks are then adapted for a one-layer
linear classifier to evaluate the performance in terms of binary aesthetic
classification. We conduct extensive quantitative experiments on three
benchmark datasets and demonstrate that our approach can faithfully extract
aesthetics-aware features and outperform alternative pretext schemes. Moreover,
we achieve comparable results to state-of-the-art supervised methods that use
10 million labels from ImageNet.Comment: AAAI Conference on Artificial Intelligence, 2020, accepte
FACTIFY3M: A Benchmark for Multimodal Fact Verification with Explainability through 5W Question-Answering
Combating disinformation is one of the burning societal crises -- about 67%
of the American population believes that disinformation produces a lot of
uncertainty, and 10% of them knowingly propagate disinformation. Evidence shows
that disinformation can manipulate democratic processes and public opinion,
causing disruption in the share market, panic and anxiety in society, and even
death during crises. Therefore, disinformation should be identified promptly
and, if possible, mitigated. With approximately 3.2 billion images and 720,000
hours of video shared online daily on social media platforms, scalable
detection of multimodal disinformation requires efficient fact verification.
Despite progress in automatic text-based fact verification (e.g., FEVER, LIAR),
the research community lacks substantial effort in multimodal fact
verification. To address this gap, we introduce FACTIFY 3M, a dataset of 3
million samples that pushes the boundaries of the domain of fact verification
via a multimodal fake news dataset, in addition to offering explainability
through the concept of 5W question-answering. Salient features of the dataset
include: (i) textual claims, (ii) ChatGPT-generated paraphrased claims, (iii)
associated images, (iv) stable diffusion-generated additional images (i.e.,
visual paraphrases), (v) pixel-level image heatmap to foster image-text
explainability of the claim, (vi) 5W QA pairs, and (vii) adversarial fake news
stories.Comment: arXiv admin note: text overlap with arXiv:2305.0432
Role of Artificial Intelligence (AI) art in care of ageing society: focus on dementia
open access articleBackground: Art enhances both physical and mental health wellbeing. The health
benefits include reduction in blood pressure, heart rate, pain perception and briefer
inpatient stays, as well as improvement of communication skills and self-esteem. In
addition to these, people living with dementia benefit from reduction of their noncognitive,
behavioural changes, enhancement of their cognitive capacities and being
socially active.
Methods: The current study represents a narrative general literature review on
available studies and knowledge about contribution of Artificial Intelligence (AI) in
creative arts.
Results: We review AI visual arts technologies, and their potential for use among
people with dementia and care, drawing on similar experiences to date from
traditional art in dementia care.
Conclusion: The virtual reality, installations and the psychedelic properties of the AI
created art provide a new venue for more detailed research about its therapeutic use in
dementia
Latent Disentanglement for the Analysis and Generation of Digital Human Shapes
Analysing and generating digital human shapes is crucial for a wide variety of applications ranging from movie production to healthcare. The most common approaches for the analysis and generation of digital human shapes involve the creation of statistical shape models. At the heart of these techniques is the definition of a mapping between shapes and a low-dimensional representation. However, making these representations interpretable is still an open challenge. This thesis explores latent disentanglement as a powerful technique to make the latent space of geometric deep learning based statistical shape models more structured and interpretable. In particular, it introduces two novel techniques to disentangle the latent representation of variational autoencoders and generative adversarial networks with respect to the local shape attributes characterising the identity of the generated body and head meshes. This work was inspired by a shape completion framework that was proposed as a viable alternative to intraoperative registration in minimally invasive surgery of the liver. In addition, one of these methods for latent disentanglement was also applied to plastic surgery, where it was shown to improve the diagnosis of craniofacial syndromes and aid surgical planning
Privacy Intelligence: A Survey on Image Sharing on Online Social Networks
Image sharing on online social networks (OSNs) has become an indispensable
part of daily social activities, but it has also led to an increased risk of
privacy invasion. The recent image leaks from popular OSN services and the
abuse of personal photos using advanced algorithms (e.g. DeepFake) have
prompted the public to rethink individual privacy needs when sharing images on
OSNs. However, OSN image sharing itself is relatively complicated, and systems
currently in place to manage privacy in practice are labor-intensive yet fail
to provide personalized, accurate and flexible privacy protection. As a result,
an more intelligent environment for privacy-friendly OSN image sharing is in
demand. To fill the gap, we contribute a systematic survey of 'privacy
intelligence' solutions that target modern privacy issues related to OSN image
sharing. Specifically, we present a high-level analysis framework based on the
entire lifecycle of OSN image sharing to address the various privacy issues and
solutions facing this interdisciplinary field. The framework is divided into
three main stages: local management, online management and social experience.
At each stage, we identify typical sharing-related user behaviors, the privacy
issues generated by those behaviors, and review representative intelligent
solutions. The resulting analysis describes an intelligent privacy-enhancing
chain for closed-loop privacy management. We also discuss the challenges and
future directions existing at each stage, as well as in publicly available
datasets.Comment: 32 pages, 9 figures. Under revie
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