Lightweight Multimodal Text Generation With Vision Understanding

via “image-to-text sequence generation with visual grounding”

image-to-text model by undefined. 75,19,420 downloads.

Unique: Implements cross-attention between visual patch embeddings and text token representations during decoding, allowing the model to dynamically reference image regions while generating text — unlike simpler CNN-to-RNN approaches that encode the entire image once

vs others: Provides better layout-aware extraction than CLIP-based approaches because it maintains visual grounding throughout decoding, while being more efficient than large multimodal models like GPT-4V due to smaller parameter count and local deployment