Large-Scale Vision Models for Robust Image Recognition and Zero-Shot Classification

Large-scale language models have revolutionized artificial intelligence by enabling AI to understand and generate natural language without extensive training on specific tasks. These models are trained using masked language modeling and next-word prediction techniques with data sourced from public repositories like Wikipedia and Common Crawl. The most notable large language model is LLaMA [14], which offers several versions with varying parameters, all trained on vast amounts of text data. ChatGPT [15] is another significant player in this advancement, trained on a massive corpus of text data, including books, articles, and web pages. This has allowed ChatGPT to develop an extensive understanding of natural language and generate text that closely resembles human language when prompted.
One exciting development in this field is the use of pre-training models for image recognition tasks. CLIP [24] is a contrastive language-image pre-training model that maximizes similarity among positive samples while minimizing similarity among negative ones, enabling the development of meaningful visual-semantic representations. Inspired by the potential of modern pre-training methods to benefit from aggregate supervision in web-scale text collections, OpenAI utilized web data instead of crowd-labeled datasets like ImageNet to create OpenAI-CLIP [19]. This approach has led to zero-shot performance that is more resilient to distribution shifts than standard ImageNet models.
Another promising foundation approach is ChestXRayBERT [16], which utilizes a pre-trained BERT-based language model to automatically generate the impression section of chest radiology reports. This approach has the potential to significantly reduce the workload of radiologists and enhance communication between radiologists and referring physicians, as demonstrated in experiments where ChestXRayBERT outperforms existing state-of-the-art models in terms of readability, factual correctness, informativeness, and redundancy.
In summary, large language models have transformed artificial intelligence by enabling AI to understand and generate natural language without extensive training on specific tasks. These models are trained using vast amounts of text data from public repositories, and their capabilities are being leveraged for image recognition tasks like zero-shot classification and image retrieval. Additionally, pre-training models like CLIP and ChestXRayBERT demonstrate the potential for aggregate supervision in web-scale text collections to create more robust large multi-modal models that can excel in various visual and language tasks. These advancements have the potential to significantly enhance communication between AI and humans, improving our ability to understand and generate natural language.