The field of vision-language models is moving towards increased integration of multimodal techniques, enabling more accurate and efficient processing of complex data. This is evident in the development of frameworks that combine textual and visual inputs to generate high-quality outputs, such as patent specifications and clinical reports. The use of multimodal architectures is also being explored in various applications, including license plate recognition, document parsing, and reasoning in latent space. Noteworthy papers in this area include: PatentVision, which enhances accuracy by combining fine-tuned vision language models with domain-specific training tailored to patents. PaddleOCR-VL, a resource-efficient model that achieves state-of-the-art performance in multilingual document parsing. NEO, a novel family of native VLMs that rival top-tier modular counterparts across diverse real-world scenarios.
Multimodal Advances in Vision-Language Models
Sources
PatentVision: A multimodal method for drafting patent applications
Patentformer: A demonstration of AI-assisted automated patent drafting
Automated Glaucoma Report Generation via Dual-Attention Semantic Parallel-LSTM and Multimodal Clinical Data Integration
Hybrid OCR-LLM Framework for Enterprise-Scale Document Information Extraction Under Copy-heavy Task
Layout-Independent License Plate Recognition via Integrated Vision and Language Models
Reasoning in the Dark: Interleaved Vision-Text Reasoning in Latent Space
PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model
From Pixels to Words -- Towards Native Vision-Language Primitives at Scale