What is Q&A with Input Image API?

‍Question Answering (Q&A) with Input Image, also known as Visual Question Answering (VQA), is a sophisticated technology that employs computer vision and natural language processing to enable the answering of questions related to images.

Typically, the input consists of an image and a textual question. The output is a text-based answer, which can be generated through open-ended questions that require the model to produce natural language answers, or through multiple-choice questions, whereby the model selects the correct answer from a predefined set of options.

However, the main purpose of VQA is to address image-related inquiries, without involving ongoing dialogues. In contrast, Chat with Input Image focuses on text-based interactions that make use of images as contextual hints or for specific inquiries within the conversation.

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Visual Question Answering APIs use cases

You can use Visual Question Answering in numerous fields, here are some examples of common use cases:

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1. Education: VQA APIs could be incorporated into academic platforms enabling pupils to raise queries about instructive pictures, diagrams, and archival photographs, hence boosting their understanding and involvement with pictorial content.
2. Healthcare Diagnostics: In the medical field, VQA can aid doctors and clinicians in the interpretation of medical images. Physicians can pose queries such as, "Is there evidence of a fracture in this X-ray?" or "What is the diagnosis based on this MRI scan?”
3. E-commerce and Product Information: In e-commerce, customers frequently inquire about image-displayed products. VQA can supply responses to inquiries such as; "What are the measurements of this settee?" or "Is this purse available in brown?”
4. Travel and Tourism: Travellers can enquire about landmarks, sights and community traditions by displaying images they come across during their journey, which can aid them in planning their itinerary more efficiently.
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Best Q&A with Input Image APIs on the market

While comparing Q&A with Input Image APIs, it is crucial to consider different aspects, among others, cost security and privacy. VQA experts at Eden AI tested, compared, and used many Q&A with Input Image APIs of the market. Here are some actors that perform well (in alphabetical order):

• AlephAlpha
• Google Cloud
• OpenAI ‍

1. AlephAlpha (Luminous) - Available on Eden AI

Aleph Alpha provides an advanced Visual Question Answering API. As part of the Luminous series, which includes a family of Aleph Alpha LLMs, these models have been extensively trained on significant amounts of human text data. Some models possess multimodal capabilities, enabling them to comprehend not only text but also images.

Their multimodal models can identify elements in pictures and comprehend contextual information, providing high-level information. This allows for the simultaneous completion of picture recognition and image interpretation.

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2. Google Cloud (Imagenen & Gemini) - Available on Eden AI

Google Cloud's Visual Question Answering (VQA) API enables users to input an image into the model and inquire about its contents. The improvement of the tool's accessibility could facilitate an increased rate of success in the user's design, analysis, or research projects. The system then generates one or more natural language responses to the question.

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3. OpenAI GPT 4 Vision - Available on Eden AI

GPT-4 is a robust multimodal model (distinct from a VQA-dedicated API) accepting both image and text inputs and delivering text outputs. Users can prompt GPT-4 with a mix of text and images for tasks involving vision and language, generating text outputs like natural language or code. Its capabilities extend to diverse domains, encompassing documents with text and images, such as photographs, diagrams, or screenshots, which makes it a perfect candidate for VQA.

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Performance Variations of Q&A with Input Image

Visual Question Answering API performance can vary depending on several variables, including the technology used by the provider, the underlying algorithms, the amount of the dataset, the server architecture, and network latency. Listed below are a few typical performance discrepancies between several Q&A with Input Image APIs:

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1. Data Quality and Diversity: The variety and quality of training data have a notable influence on VQA performance. When the scope of the training data is limited or it includes biases, the system may struggle with questions and images that differ from the distribution of the training data.
2. Support for Different Image Formats: Consider whether the API supports a variety of image formats and resolutions, as this can impact its usability in different applications.
3. Latency and Throughput: The speed at which the API processes visual questions and generates answers (latency) and the number of requests it can handle concurrently (throughput) are important considerations, especially for real-time applications.
4. Fine-Tuning: Some VQA APIs allow for fine-tuning on specific datasets or domains. Fine-tuning the model on relevant data can improve its performance for specific use cases.
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Why choose Eden AI to manage your VQA APIs

Companies and developers from a wide range of industries (Social Media, Retail, Health, Finances, Law, etc.) use Eden AI’s unique API to easily integrate Image Question Answering tasks in their cloud-based applications, without having to build their solutions.

Eden AI offers multiple AI APIs on its platform among several technologies: Text-to-Speech, Language Detection, Sentiment Analysis, Face Recognition, Question Answering, Data Anonymization, Speech Recognition, and so forth.

We want our users to have access to multiple VQA engines and manage them in one place so they can reach high performance, optimize cost, and cover all their needs. There are many reasons for using multiple APIs :

- Fallback provider is the ABCs: You need to set up a provider API that is requested if and only if the main VQA API does not perform well (or is down). You can use the confidence score returned or other methods to check provider accuracy.
- Performance optimization: After the testing phase, you will be able to build a mapping of providers’ performance based on the criteria you have chosen (languages, fields, etc.). Each data that you need to process will then be sent to the best VQA.‍
- Cost - Performance ratio optimization: You can choose the cheapest VQA provider that performs well for your data.
- Combine multiple AI APIs: This approach is required if you look for extremely high accuracy. The combination leads to higher costs but allows your AI service to be safe and accurate because VQA APIs will validate and invalidate each other for each piece of data.
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How Eden AI can help you?

Eden AI is the future of AI usage in companies: our app allows you to call multiple AI APIs.

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• Centralized and fully monitored billing on Eden AI for all VQA APIs.
• Unified API for all providers: simple and standard to use, quick switch between providers, access to the specific features of each provider.
• Standardized response format: the JSON output format is the same for all suppliers thanks to Eden AI's standardization work. The response elements are also standardized thanks to Eden AI's powerful matching algorithms.
• The best Artificial Intelligence APIs in the market are available: big cloud providers (Google, AWS, Microsoft, and more specialized engines).
• Data protection: Eden AI will not store or use any data. Possibility to filter to use only GDPR engines.

You can see Eden AI documentation here.

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Next step in your project

The Eden AI team can help you with your VQA integration project. This can be done by:

• Organizing a product demo and a discussion to better understand your needs. You can book a time slot on this link: Contact
• By testing the public version of Eden AI for free: however, not all providers are available on this version. Some are only available on the Enterprise version.
• By benefiting from the support and advice of a team of experts to find the optimal combination of providers according to the specifics of your needs.
• Having the possibility to integrate on a third-party platform: we can quickly develop connectors.

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