VLM & VQA

Traditional computer vision models (YOLO, ResNet, EfficientNet) are trained for specific tasks — detecting objects, classifying images, measuring dimensions — and output structured data (bounding boxes, class labels, scores). Vision Language Models (VLMs) combine a vision encoder with a large language model, enabling them to understand images holistically and respond in free-form natural language. VLMs excel at complex reasoning, context-aware descriptions, answering arbitrary questions, and handling novel visual scenarios — but they're slower and costlier than specialised CV models. We help you choose the right approach — or combine both — depending on your accuracy, latency, and budget requirements.

The choice depends on your use case, data privacy requirements, and budget. GPT-4V and Claude Vision offer the highest accuracy for complex reasoning and are ideal for applications where cloud processing is acceptable. Gemini Vision excels in multi-modal document tasks. Open-source models like LLaVA, InternVL, and Phi-3 Vision allow on-premise deployment for sensitive data, are more cost-effective at high volumes, and can be fine-tuned on proprietary visual data. We benchmark multiple models against your specific images and tasks before recommending a solution, and we design systems that can route requests to the optimal model based on complexity and cost.

Yes — open-source VLMs can be fine-tuned using supervised learning on your labelled image-question-answer pairs. Using LoRA and QLoRA techniques, we can adapt models like LLaVA, InternVL, or Idefics to your specific domain (medical imaging, industrial equipment, branded products) with relatively small datasets — typically 500–5,000 high-quality examples. Fine-tuned models dramatically outperform general VLMs on domain-specific tasks and can be deployed on your own infrastructure. Proprietary VLMs (GPT-4V, Claude Vision) cannot be fine-tuned by third parties, but can be improved through advanced prompting, RAG with visual context, and structured output techniques.

Accuracy depends heavily on the task complexity and the quality of prompting or fine-tuning. For well-defined tasks like document field extraction, product identification, or defect detection, fine-tuned VLMs typically achieve high accuracy. For open-ended visual reasoning and complex scene interpretation, accuracy varies and hallucination is a known risk. We address this through multi-model voting ensembles, confidence calibration, structured output validation, retrieval-augmented visual context, and human-in-the-loop review for low-confidence predictions. We establish clear accuracy baselines and thresholds before production deployment and provide ongoing monitoring dashboards.

Our VLM pipelines support standard image formats (JPEG, PNG, WebP, TIFF, BMP) as well as PDFs with mixed content, video frames extracted at configurable intervals, medical imaging formats (DICOM with pre-processing), and high-resolution images with tiling strategies for models with context window size limitations. We implement image pre-processing pipelines for optimising resolution, contrast, and format conversion to match each VLM's input requirements, ensuring maximum accuracy regardless of the source format or capture device.