LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognition (Supplementary Material)

In Figure 1, we compare our LMC framework with the baseline Softmax, and present qualitative results on the TinyImageNet dataset. Note that for the baseline Softmax, we do not simulate any virtual open-set classes. As shown, via simulating additional virtual open-set classes that share the spurious-discriminative features, our framework can prevent the closed-set score S of the open-set testing image from being easily overestimated by approaching the image to both a certain closed-set class and certain virtual open-set classes. This demonstrates the effectiveness of our framework in reducing the reliance on spurious-discriminative features. In our experiments, following [1, 11], we use the following two metrics: AUROC and OSCR [3].