Incomplete In-context Learning

Abstract

Large vision language models (LVLMs) achieve remarkable performance through Vision In-context Learning (VICL), a process that depends significantly on demonstrations retrieved from an extensive collection of annotated examples (retrieval database). Existing studies often assume that the retrieval database contains annotated examples for all labels. However, in real-world scenarios, delays in database updates or incomplete data annotation may result in the retrieval database containing labeled samples for only a subset of classes. We refer to this phenomenon as an \textbf{incomplete retrieval database} and define the in-context learning under this condition as \textbf{Incomplete In-context Learning (IICL)}. To address this challenge, we propose \textbf{Iterative Judgments and Integrated Prediction (IJIP)}, a two-stage framework designed to mitigate the limitations of IICL. The Iterative Judgments Stage reformulates an \(\boldsymbol{m}\)-class classification problem into a series of \(\boldsymbol{m}\) binary classification tasks, effectively converting the IICL setting into a standard VICL scenario. The Integrated Prediction Stage further refines the classification process by leveraging both the input image and the predictions from the Iterative Judgments Stage to enhance overall classification accuracy. IJIP demonstrates considerable performance across two LVLMs and two datasets under three distinct conditions of label incompleteness, achieving the highest accuracy of 93.9\%. Notably, even in scenarios where labels are fully available, IJIP still achieves the best performance of all six baselines. Furthermore, IJIP can be directly applied to \textbf{Prompt Learning} and is adaptable to the \textbf{text domain}.