Section 4 summarizes efficient methods for multimodal large-scale pre-trained models from three aspects: parameter-efficient transfer, memory-efficient training, and efficient data utilization. Section 5 highli
Large language models (LLMs) are seen to have tremendous potential in advancing medical diagnosis recently, particularly in dermatological diagnosis, which is a very important task as skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases. Her...
Here we present SkinGPT-4, which is an interactive dermatology diagnostic system based on multimodal large language models. We have aligned a pre-trained vision transformer with an LLM named Llama-2-13b-chat by collecting an extensive collection of skin disease images (comprising 52,929 publicly ...
Artificial writing is permeating our lives due to recent advances in large-scale, transformer-based language models (LMs) such as BERT, GPT-2 and GPT-3. Using them as pre-trained models and fine-tuning them for specific tasks, researchers have extended the state of the art for many natural...
With the prevalence of pre-trained language models (PLMs) and the pre-training–fine-tuning paradigm, it has been continuously shown that larger models tend to yield better performance. However, as PLMs scale up, fine-tuning and storing all the parameters is prohibitively costly and eventually be...
1、大模型的定义 大模型(Large Models)通常指参数规模庞大(通常在十亿到万亿级别)的深度学习模型。这类模型通过在大规模数据集上进行训练,具备强大的泛化能力和复杂的任务处理能力,尤… 大模型 大模型的研究新方向:混合专家模型(MoE) Deep ...发表于人工智能应... 如何通俗易懂地理解大模型参数? 大型语言模型 ...
Pre-CLIP, most vision-language models were trained using a classifier or language model objectives. Contrastive objective is a clever technique that allows CLIP to scale and generalize to multiple tasks. We’ll show why the constrastive objective works better for CLIP using an example task of imag...
Expanding large language models into the multi-sensory domain represents a remarkable convergence of AI capabilities. Here’s how LLMs are evolving to embrace multi-modal data: Multi-Modal Training Data: To tackle multi-modal tasks effectively, LLMs are trained on vast and diverse datasets that ...
🔥🔥🔥 Long-VITA: Scaling Large Multi-modal Models to 1 Million Tokens with Leading Short-Context Accuracy [📖 arXiv Paper] [🌟 GitHub] Process more than 4K frames or over 1M visual tokens. State-of-the-art on Video-MME under 20B models! ✨ 🔥🔥🔥 MM-RLHF: The Next...
Based on applications, the popular domains of large-scale models include large models of NLP (Chinese language), CV (computer vision), multi-modal models, scientific computing, etc. GPT-3, MT-NLG, and Yuan 1.0 are now popular monolithic large-scale models in NLP. The self-supervised pre-tr...