Pre Trained Vision

Pre-trained vision-language models (VLMs) aim to create joint representations of visual and textual information, enabling improved performance on downstream tasks like image classification and visual question answering. Current research focuses on enhancing VLM effectiveness through techniques such as parameter-efficient fine-tuning, improved few-shot learning strategies (e.g., active learning and novel instance selection methods), and integrating large language models (LLMs) to leverage their broader knowledge base. These advancements are significant because they improve the robustness and efficiency of VLMs, leading to more accurate and versatile applications in various fields, including image retrieval, scene understanding, and even addressing biases in existing models.