Multimodal Transformer — Text & Image Understanding Model

I will build a multimodal transformer system for your combined text and image understanding task — using CLIP, BLIP, or Flamingo-style architectures for image-text alignment, visual question answering, image captioning, or cross-modal retrieval — enabling your application to understand and relate information across visual and language modalities simultaneously. Multimodal transformers are increasingly the architecture of choice for applications that humans naturally process using both vision and language — product search that matches natural language queries to images, visual QA systems for document understanding, and image captioning for accessibility — and the right architecture depends critically on your specific task and whether you need alignment, generation, or retrieval capabilities.

The build covers task-appropriate architecture selection, image and text encoder configuration, cross-attention or alignment head implementation, fine-tuning on your dataset, evaluation appropriate to your task, and an inference API.