  In realistic open-set scenarios where labels of a part of testing data are
totally unknown, when vision-language (VL) prompt learning methods encounter
inputs related to unknown classes (i.e., not seen during training), they always
predict them as one of the training classes. The exhibited label bias causes
difficulty in open set recognition (OSR), in which an image should be correctly
predicted as one of the known classes or the unknown one. To achieve this goal,
we propose a vision-language prompt tuning method with mitigated label bias
(M-Tuning). It introduces open words from the WordNet to extend the range of
words forming the prompt texts from only closed-set label words to more, and
thus prompts are tuned in a simulated open-set scenario. Besides, inspired by
the observation that classifying directly on large datasets causes a much
higher false positive rate than on small datasets, we propose a Combinatorial
Tuning and Testing (CTT) strategy for improving performance. CTT decomposes
M-Tuning on large datasets as multiple independent group-wise tuning on fewer
classes, then makes accurate and comprehensive predictions by selecting the
optimal sub-prompt. Finally, given the lack of VL-based OSR baselines in the
literature, especially for prompt methods, we contribute new baselines for fair
comparisons. Our method achieves the best performance on datasets with various
scales, and extensive ablation studies also validate its effectiveness.
