How Computer Vision is Transforming
Marketing?
Computer vision is a powerful and revolutionary technology.
Its use cases have emerged in various industries including retail, automotive, healthcare,
defense, agriculture, banking and marketing.
Computer vision provides exciting capabilities to marketers.
Computer vision enables computers to acquire visual understanding just like human
beings use their eyes and brains to understand their surroundings. Computer
vision works by scanning images and videos, and translating their contents into
metadata. This data can be then organized and used by the marketers in varied
ways.
Computer vision is transforming marketing in several
ways. The following are some of the ways through which computer vision is
reshaping the marketing landscape and providing awesome opportunities to
marketers –
Content Generation Through Generative
Adversarial Networks (GANs)
Creation of new content is one of the greatest
challenges faced by online marketers. Generative Adversarial Network (GAN) is
an approach to generative modelling, which implies that these networks can be
used to generate or create new visual content.
A Generative Adversarial Network is trained using a generator
model and discriminator model. The generator model learns to generate new
examples, whereas the discriminator model learns to differentiate examples as
either real or fake.
GANs can be used by marketers to create visual content
such as videos, images and three-dimensional models. For example, GANs can be
used to generate an image of a person in different poses. This is done by
providing data on different poses to the system. GANs have also been used to
generate realistic photos of fake fashion models. These examples show how GAN
can help marketers easily create original visual content through computer
vision.
Some of the other uses of GANs include generation of
realistic photographs of human faces, creation of cartoon characters, image-to-image
translation such as translation of photos of day to night or of summer to
winter, translation of text to image, etc. Marketers can use GANs to create visual
content at massive scale so as to fully meet their marketing requirements.
Product Search by Using Visual Similarity
Computer vision allows consumers to search a product
without typing keywords or product-tags in the search bar. A buyer uploads an
image of a product. The visual search technology shows him/her the same and similar
products on the screen.
This computer vision powered visual search technology
makes product finding easy and fast for consumers, and improves their shopping
experience. This technology facilitates online shopping and leads to increase
in sales.
Contextual Advertising
Computer vision-powered contextual advertising
delivers very high degree of context relevance. Computer vision enables
detection of contexts in video such as faces, emotions, logos, objects, scenes
and activities with very high accuracy, allowing marketers to display in-video
ads that are in line with the content the user is actively engaging with.
Computer vision powered contextual advertising is a
boon for marketers as it allows them to achieve unprecedented reach and user
engagement; not achievable by traditional methods of contextual advertising.
Understanding Images Through Screen Graphs
By using scene graphs, it is possible to retrieve images
by describing their contents. For example, by using this technology, one can
enter “a man wearing a black hat and sitting in a white car” in the search bar and
all the relevant images will appear in the search results. Current image retrieval
systems that do not deploy scene graphs cannot handle such complex queries.
Scene graphs represent objects, attributes of objects
and relationships between the objects in images. The scene graph-based
technology is a new field of applied computer vision. For marketers, this
technology provides benefits such as contextual image generation and
intelligent auto-captioning. Scene graphs enables marketers to make data-driven
decisions around visual content.
Brand Safety
Computer vision-powered brand safety solutions enables
marketers to effectively avoid ad placements against brand unsafe content.
These solutions work by detecting harmful or unsuitable contexts in streaming
videos. Computer vision offers a tailored approach to brands by enabling them
to custom define unsuitable contexts, thus allowing them to run their ads in a truly
brand suitable environment. Computer vision also overcomes the problems of
content under- and over-blocking inherent in the traditional brand safety
methods.
By integrating computer vision into their marketing
efforts, marketers can easily generate visual content at scale, gain deep
insights, enhance customer experiences, facilitate consumers’ buying, run
highly successful campaigns, and ensure true brand suitability.
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