Debiased Textual Prompt Tuning for Enhancing Unknown Class Discovery

Open-world semi-supervised learning (OWSSL) aims to rec-ognize known and unknown classes from unlabeled samples, where la-beled samples only include known classes. Recent studies introduce class-specific textual descriptions as semantic information and employ learn-able textual prompts to optimize these descriptions, thereby enhancingthe model’s ability to recognize classes. However, these methods oftenoverlook the model bias toward known classes during textual prompttuning, and establish only limited cross-modal connections, leading tosevere sparsity in the graph structure and hindering the propagation ofvisual and semantic information across the graph. These issues collec-tively limit the model’s ability to discover unknown classes, particularlyin scenarios with a large number of classes. To address these issues, wepropose a novel OWSSL method, which consists of two core components:(1) textual prompt optimization aimed at mitigating the model bias to-ward known classes, and (2) bimodal graph enhancement designed toalleviate the sparsity issue in the graph structure. Experimental resultsacross multiple datasets indicate that our method achieves an averageaccuracy 20.7% higher on unknown classes than state-of-the-art OWSSLmethods (relative improvement).