250 research outputs found
Multimodal knowledge integration for object detection and visual reasoning
We humans still perceive and reason in a different way than artificial intelligence models. We witness, we listen, we touch, we understand the world via multi-modal sensing, while machine models rely only on a single or a few modalities and ignore abundant information. In this thesis, we explore techniques for reducing the perception gap between machines and humans and focus on two families of tasks, reasoning and detection. First, we incorporate information from text, audio, motion, external knowledge bases, for training computer vision models. We find that data inputs from more extensive channels provide complementary information to improve models. Second, we study how multimodal inputs can be fully utilized. We argue that most existing deep learning methods are prone to pay too large attention to shallow patterns in the input features, which causes the resulting models to be biased. We propose robust training to overcome the issue. Third, we extend the benefits of multi-modal information to the supervision signals instead of the inputs, by learning a weakly supervised detection model from the natural supervision of textual captions or audio narrations. With the help of NLP constituency parsing, it is possible to extract structural knowledges from the captions and narrations, hence determines the entities and relations of visual objects
Automatic Understanding of Image and Video Advertisements
There is more to images than their objective physical content: for example,
advertisements are created to persuade a viewer to take a certain action. We
propose the novel problem of automatic advertisement understanding. To enable
research on this problem, we create two datasets: an image dataset of 64,832
image ads, and a video dataset of 3,477 ads. Our data contains rich annotations
encompassing the topic and sentiment of the ads, questions and answers
describing what actions the viewer is prompted to take and the reasoning that
the ad presents to persuade the viewer ("What should I do according to this ad,
and why should I do it?"), and symbolic references ads make (e.g. a dove
symbolizes peace). We also analyze the most common persuasive strategies ads
use, and the capabilities that computer vision systems should have to
understand these strategies. We present baseline classification results for
several prediction tasks, including automatically answering questions about the
messages of the ads.Comment: To appear in CVPR 2017; data available on
http://cs.pitt.edu/~kovashka/ad
What Explains Natives and Sojourners Preventive Health Behavior in a Pandemic: Role of Media and Scientific Self-Efficacy
The COVID-19 pandemic triggered a severe global public health emergency. The current research investigated and compared “Natives and Sojourners” health-protective behavior in Mainland China during the pandemic. We adopted a unified view to propose our theoretical model by adapting the Health Belief Model (HBM) and Institutional Theory (IT). The data obtained through an online survey questionnaire from 435 respondents during the second and third quarters of were analyzed. Structural equation modeling (SEM) was used to empirically analyze the proposed model. The media self-efficacy (MSE), scientific self-efficacy (SSE), perceived health risks (PHRs), and the perceived benefits of being protected have positive and significant effects on the definition of health-protective behavioral intentions among natives and sojourners in mainland China. Media and SSE can play a strategic role in formulating public health-protective behavior. The current research recommends an effective communication with sojourners during crisis for them to be a part of the national crisis management plan (i.e., infectious disease)
VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining
Assessing the aesthetics of an image is challenging, as it is influenced by
multiple factors including composition, color, style, and high-level semantics.
Existing image aesthetic assessment (IAA) methods primarily rely on
human-labeled rating scores, which oversimplify the visual aesthetic
information that humans perceive. Conversely, user comments offer more
comprehensive information and are a more natural way to express human opinions
and preferences regarding image aesthetics. In light of this, we propose
learning image aesthetics from user comments, and exploring vision-language
pretraining methods to learn multimodal aesthetic representations.
Specifically, we pretrain an image-text encoder-decoder model with
image-comment pairs, using contrastive and generative objectives to learn rich
and generic aesthetic semantics without human labels. To efficiently adapt the
pretrained model for downstream IAA tasks, we further propose a lightweight
rank-based adapter that employs text as an anchor to learn the aesthetic
ranking concept. Our results show that our pretrained aesthetic vision-language
model outperforms prior works on image aesthetic captioning over the
AVA-Captions dataset, and it has powerful zero-shot capability for aesthetic
tasks such as zero-shot style classification and zero-shot IAA, surpassing many
supervised baselines. With only minimal finetuning parameters using the
proposed adapter module, our model achieves state-of-the-art IAA performance
over the AVA dataset.Comment: CVPR 2023,
https://github.com/google-research/google-research/tree/master/vil
Recommending Themes for Ad Creative Design via Visual-Linguistic Representations
There is a perennial need in the online advertising industry to refresh ad
creatives, i.e., images and text used for enticing online users towards a
brand. Such refreshes are required to reduce the likelihood of ad fatigue among
online users, and to incorporate insights from other successful campaigns in
related product categories. Given a brand, to come up with themes for a new ad
is a painstaking and time consuming process for creative strategists.
Strategists typically draw inspiration from the images and text used for past
ad campaigns, as well as world knowledge on the brands. To automatically infer
ad themes via such multimodal sources of information in past ad campaigns, we
propose a theme (keyphrase) recommender system for ad creative strategists. The
theme recommender is based on aggregating results from a visual question
answering (VQA) task, which ingests the following: (i) ad images, (ii) text
associated with the ads as well as Wikipedia pages on the brands in the ads,
and (iii) questions around the ad. We leverage transformer based cross-modality
encoders to train visual-linguistic representations for our VQA task. We study
two formulations for the VQA task along the lines of classification and
ranking; via experiments on a public dataset, we show that cross-modal
representations lead to significantly better classification accuracy and
ranking precision-recall metrics. Cross-modal representations show better
performance compared to separate image and text representations. In addition,
the use of multimodal information shows a significant lift over using only
textual or visual information.Comment: 7 pages, 8 figures, 2 tables, accepted by The Web Conference 202
Precursors and Pathways Leading to Enhanced Secondary Organic Aerosol Formation during Severe Haze Episodes
Publisher Copyright: © 2021 American Chemical SocietyMolecular analyses help to investigate the key precursors and chemical processes of secondary organic aerosol (SOA) formation. We obtained the sources and molecular compositions of organic aerosol in PM2.5in winter in Beijing by online and offline mass spectrometer measurements. Photochemical and aqueous processing were both involved in producing SOA during the haze events. Aromatics, isoprene, long-chain alkanes or alkenes, and carbonyls such as glyoxal and methylglyoxal were all important precursors. The enhanced SOA formation during the severe haze event was predominantly contributed by aqueous processing that was promoted by elevated amounts of aerosol water for which multifunctional organic nitrates contributed the most followed by organic compounds having four oxygen atoms in their formulae. The latter included dicarboxylic acids and various oxidation products from isoprene and aromatics as well as products or oligomers from methylglyoxal aqueous uptake. Nitrated phenols, organosulfates, and methanesulfonic acid were also important SOA products but their contributions to the elevated SOA mass during the severe haze event were minor. Our results highlight the importance of reducing nitrogen oxides and nitrate for future SOA control. Additionally, the formation of highly oxygenated long-chain molecules with a low degree of unsaturation in polluted urban environments requires further research.Peer reviewe
Electronic properties of guanine-based nanowires
We present a first-principle study of the electronic and conduction
properties of a few classes of nanowires constituted of guanine (G) molecules,
self-assembled in different geometries. We first analyze the effect of the
vertical - interaction in model G-stack columns. Then, we exploit the
results obtained from those models to interpret the features of realistic
stacked and hydrogen-bonded structures, namely the guanine quadruple helices
and the planar ribbons. With respect to natural DNA, the different structures
as well as the inclusion of metal cations, drastically affect the bonding
pattern among the bases, introducing novel features in the electronic
properties of the systems. These supramolecular G-aggregates, alternative to
DNA, are expected to show intersting properties for molecular elec tronics
applications.Comment: 30 pages (preprint format), 8 figures. To appear in Solid State
Communications - Special Issue on "New advances on collective phenomena in
one-dimensional systems
- …