Abstract
Current state-of-the-art Wildlife classification models are trained
under the closed world setting. When exposed to unknown classes, they
remain overconfident in their predictions. Open-set Recognition (OSR)
aims to classify known classes while rejecting unknown samples. Several
OSR methods have been proposed to model the closed-set distribution
by observing the feature, logit, or softmax probability space. A significant
drawback of many existing approaches is the requirement to retrain
the pre-trained classification model with the OSR-specific strategy.
This study contributes a post-processing OSR method that measures
the agreement between the models’ features and predicted logits. We
propose a probability distribution based on an input’s distance to its
Nearest Class Mean (NCM). The NCM-based distribution is then compared
with the softmax probabilities from the logit space to measure
agreement between the NCM and the classification head. Our proposed
strategy ranks within the top three on two evaluated datasets, showing
consistent performance across the two datasets. In contrast, current
state-of-the-art methods excel on a single dataset. We achieve an AUROC
of 93.41 and 95.35 for African and Swedish animals. The code will
be released publicly upon acceptance of this paper.