For a long time, computer vision relied on training a bespoke AI model to recognize either objects or classify images into categories. For example, if you intended to identify characters via OCR you need to train the system to recognize the characters in your scope. Or, for image classification, a typical example would be to identify if an image contains nudity or not.
With the emergence of VQA however, there is no longer a requirement to train either object recognition nor classification - in a way. There are still cases it is useful but we get to that one later. VQA stands for “Visual Questions and Answers” and it is based on a different approach to visual data processing. This innovative technique involves an AI system processing an image and then responding to questions about the image in natural language. It combines techniques from both computer vision and natural language processing to interpret and answer questions about visual content.
This technology has significant implications for how we interact with AI in image-related tasks. For instance, instead of simply identifying objects in a picture, VQA systems can answer complex questions about the relationships between objects, their attributes, and even infer context or emotions depicted in the image. This makes VQA a more interactive and versatile tool compared to traditional image recognition models.
Moreover, VQA systems are trained on a diverse range of images and questions, which enables them to develop a deeper understanding of both visual content and language semantics. This dual focus helps them to better understand and respond to a wide variety of queries, making them highly adaptable to different applications. From aiding visually impaired individuals in understanding their surroundings to enhancing educational tools with interactive visual content, the potential uses of VQA are vast and varied.
However, it's important to note that while VQA reduces the need for specific object recognition or classification training, these traditional methods still have their place. In scenarios where precise identification of objects is crucial, such as in medical imaging or security surveillance, the specific training for object recognition and classification remains essential. The VQA, in these cases, acts as an additional layer of interpretation and interaction, rather than a replacement.
A VQA system requires a substantial amount of computing power. Therefore it is also relatively slow. Too slow to be used in real time. For example, if you like to detect if people walking through a door are wearing safety helmets and other PPE, VQA would be too slow. Also, the required computing power would be too intense to conduct such basic tasks. In such cases, VQA can be used to train a bespoke computer vision model using so-called “auto labeling”. The training data, alongside with the VQA trained labels, can then be used to train a model which can also be deployed at the edge, as it uses fewer resources while yet being able to recognize objects and/or classify images more or less instantly.
Another weakness of the VQA technology is its susceptibility to errors arising from ambiguous or poorly defined questions. Unlike humans who can use context or ask for clarifications when faced with unclear queries, VQA systems might struggle or provide incorrect responses. This limitation is particularly evident in scenarios where the questions involve abstract concepts, subjective interpretations, or require inferences beyond the visual data presented.
Additionally, VQA systems are often trained on specific datasets, and their performance can significantly drop when exposed to images or question types outside their training scope. This lack of generalizability means they might not perform well in diverse real-world situations where the visual scenes and questions can vary greatly.
Moreover, VQA systems can inadvertently propagate biases present in their training data. If the dataset contains biases related to race, gender, or cultural backgrounds, the system's responses could reinforce these biases, leading to ethical concerns and potentially harmful consequences, especially in sensitive applications.
In another sense of sensitivity, using an off the shelf generative AI product like OpenAI may also mean that the images or data you upload to them will be used for further training and improvement of the AI system. This needs to be taken into consideration in terms of privacy of your data. Depending on the plan, there are enterprise plans where you can keep your data private with OpenAI and will not be used for further training of their model.
Furthermore, the reliance on large, annotated datasets for training presents another challenge. Collecting and labeling these datasets is a resource-intensive task. While auto-labeling can mitigate this to an extent, the initial setup and maintenance of a reliable auto-labeling system add to the overall complexity and cost.
Finally, VQA's heavy reliance on technological sophistication means that it may not be accessible or feasible for all users or organizations, especially those with limited technical infrastructure or expertise. This limitation can create a divide in who can effectively use and benefit from this technology.
Gravio 5.2 and newer com with an off-the-shelf VQA integration into OpenAI’s platform. All you need is an API key (learn how to get your own OpenAI API key). With Gravio you can send a picture, either from a camera, screenshot or any other source to OpenAI alongside with a prompt. The reply from OpenAI can then be used in further components within Gravio. An example is to force OpenAI to reply in a CSV format, and then write the data replied into a CSV file.
In the future, as we progress further with generative AI technologies, there will be instances where you can deploy these AI Large Language Models (LLM) at the edge or in a private cloud. In fact, Microsoft Azure already offers this private cloud OpenAI service. This way, you can have a completely closed system which can be achieved by using Gravio and Microsoft Azure OpenAI.
We consider VQA as a significant step in the AI/Computer Vision industry, simply because it allows a computer system to make sense of visual data without pre-training it for specific tasks. Some of the use cases include visual inspection of products, identifying the nature of situations, and providing real-time solutions or feedback. This capability opens up numerous possibilities for businesses in various sectors.
VQA to Enhance Customer Service: In the retail and service industries, VQA can be utilized to offer advanced customer support. Customers can simply upload an image and ask questions about a product, its usage, or troubleshooting, and receive instant, accurate responses. This not only improves customer experience but also reduces the workload on human customer service representatives.
VQA for Improved Accessibility: VQA technology can revolutionize accessibility, particularly for visually impaired individuals. Businesses can integrate VQA into their apps or websites, allowing users to understand their surroundings or get information about products just by taking a picture.
VQA for Quality Control in Manufacturing: In manufacturing, VQA can be used for quality control processes. By analyzing images of products on the assembly line, VQA can identify defects, deviations, or inconsistencies, thereby reducing the margin of error and ensuring high-quality output.
VQA in Healthcare Applications: In healthcare, VQA can assist in diagnostic procedures. For instance, it can help in analyzing medical images such as X-rays or MRIs, providing quick preliminary assessments or highlighting areas that require a doctor's attention.
VQA in Retail and Inventory Management: In retail, VQA can be used for inventory management by identifying products on shelves, tracking their quantities, and even providing insights into shopping trends based on visual data analysis.
VQA in Safety and Surveillance: In the field of safety and surveillance, VQA can analyze video feeds in real-time to identify potential safety hazards, unauthorized activities, or emergency situations, enabling prompt responses.
VQA in Agriculture and Environmental Monitoring: For agriculture, VQA can analyze images from drones or satellites to assess crop health, growth patterns, or detect pest infestations, thereby aiding in precision agriculture. Similarly, it can be used for environmental monitoring, like analyzing changes in ecosystems or tracking wildlife.
VQA for Education and Training: In education, VQA can provide an interactive learning experience, where students can learn about objects, phenomena, or historical artifacts by querying through images.
VQA in the Automotive Industry: In the automotive sector, VQA can be integrated into driver assistance systems to interpret road scenes and provide real-time feedback, enhancing safety and driving experience.
VQA in Marketing and Consumer Insights: For marketing, VQA can analyze consumer behavior through visual data, like how customers interact with products, helping businesses tailor their marketing strategies accordingly.
In conclusion, the integration of VQA into business operations can revolutionize how companies interact with their customers, manage their products, and make data-driven decisions. Its ability to understand and interpret visual data in a human-like manner opens up a new realm of possibilities, making businesses more efficient, responsive, and adaptive to consumer needs. See you in the next one!