CAMformer: Associative Memory is All You Need

A recent study analyzed the multi-modal approach in the Vision Transformer (EVA-ViT) image encoder combined with LlaMA and ChatGPT large language models (LLMs) to address hallucination issues and enhance disease detection in chest X-ray images. The research utilized the NIH Chest X-ray dataset, comparing image-based and text-based retrieval-augmented generation (RAG) methods, revealing that text-based RAG effectively mitigates hallucinations while image-based RAG improves prediction confidence.

A novel deep learning framework named TIMM-ProRS has been introduced to enhance the prognosis and risk stratification of diabetic retinopathy (DR), a condition that threatens the vision of millions worldwide. This framework integrates Vision Transformer, Convolutional Neural Network, and Graph Neural Network technologies, utilizing both retinal images and temporal biomarkers to achieve a high accuracy rate of 97.8% across multiple datasets.

A novel Price-Incentive Mechanism (PRINCE) has been proposed to enhance Multi-Tenant Split Federated Learning (SFL) for Foundation Models (FMs) like GPT-4, enabling efficient fine-tuning on resource-constrained devices while maintaining privacy. This mechanism addresses the coordination challenges faced by multiple SFL tenants with diverse fine-tuning needs.

Recent advancements in Uniform Meaning Representation (UMR) have led to the exploration of methods for generating text from multilingual UMR graphs, enhancing the capabilities of semantic representation in natural language processing. This research aims to develop a technological ecosystem around UMR, building on the existing frameworks of Abstract Meaning Representation (AMR).