Visual Question and Answering

Visual Question Answering (VQA) is the task of answering open-ended questions based on an image. The input to models supporting this task is typically a combination of an image and a question, and the output is an answer expressed in natural language.

Some noteworthy use case examples for VQA include:

• Accessibility applications for visually impaired individuals.

• Education: posing questions about visual materials presented in lectures or textbooks. VQA can also be utilized in interactive museum exhibits or historical sites.

• Customer service and e-commerce: VQA can enhance user experience by letting users ask questions about products.

• Image retrieval: VQA models can be used to retrieve images with specific characteristics. For example, the user can ask “Is there a dog?” to find all images with dogs from a set of images.

Table of Contents

1. Architecture

2. Deployment Options

3. Validated Configurations

Architecture

The VisualQnA example is implemented using the component-level microservices defined in GenAIComps. The flow chart below shows the information flow between different microservices for this example.

flowchart LR %% Colors %% classDef blue fill:#ADD8E6,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5 classDef orange fill:#FBAA60,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5 classDef orchid fill:#C26DBC,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5 classDef invisible fill:transparent,stroke:transparent; style VisualQnA-MegaService stroke:#000000 %% Subgraphs %% subgraph VisualQnA-MegaService["VisualQnA MegaService "] direction LR LVM([LVM MicroService]):::blue end subgraph UserInterface[" User Interface "] direction LR a([User Input Query]):::orchid Ingest([Ingest data]):::orchid UI([UI server<br>]):::orchid end LVM_gen{{LVM Service <br>}} GW([VisualQnA GateWay<br>]):::orange NG([Nginx MicroService]):::blue %% Questions interaction direction LR Ingest[Ingest data] --> UI a[User Input Query] --> |Need Proxy Server|NG a[User Input Query] --> UI NG --> UI UI --> GW GW <==> VisualQnA-MegaService %% Embedding service flow direction LR LVM <-.-> LVM_gen

This example guides you through how to deploy a LLaVA-NeXT (Open Large Multimodal Models) model on Intel Gaudi2 and Intel Xeon Scalable Processors. We invite contributions from other hardware vendors to expand the OPEA ecosystem.

Deployment Options

The VisualQnA service can be effortlessly deployed on either Intel Gaudi2 or Intel Xeon Scalable Processors.

The table below lists currently available deployment options. They outline in detail the implementation of this example on selected hardware.

Category	Deployment Option	Description
On-premise Deployments	Docker compose	VisualQnA deployment on Xeon
		VisualQnA deployment on Gaudi
		VisualQnA deployment on AMD ROCm
	Kubernetes	Helm Charts
		GMC

Validated Configurations

Deploy Method	LLM Engine	LLM Model	Hardware
Docker Compose	TGI, vLLM	llava-hf/llava-v1.6-mistral-7b-hf	Intel Xeon
Docker Compose	TGI, vLLM	llava-hf/llava-1.5-7b-hf	Intel Gaudi
Docker Compose	TGI, vLLM	Xkev/Llama-3.2V-11B-cot	AMD ROCm
Helm Charts	TGI, vLLM	llava-hf/llava-v1.6-mistral-7b-hf	Intel Gaudi
Helm Charts	TGI, vLLM	llava-hf/llava-v1.6-mistral-7b-hf	Intel Xeon