From Coarse to Fine-Grained Open-Set Recognition
Authors: Nico Lang, Vésteinn Snæbjarnarson, Elijah Cole, Oisin Mac Aodha, Christian Igel, Serge Belongie

Open-set recognition (OSR) methods aim to identify whether or not a test example belongs to a category ob- served during training. Depending on how visually sim- ilar a test example is to the training categories the OSR task can be easy or extremely challenging. However the vast majority of previous work has studied OSR in the presence of large coarse-grained semantic shifts. In contrast many real-world problems are inherently fine- grained which means that test examples may be highly visually similar to the training categories. Motivated by this observation we investigate three aspects of OSR: label granularity similarity between the open- and closed-sets and the role of hierarchical supervision during training. To study these dimensions we curate new open-set splits of a large fine-grained visual categorization dataset. Our anal- ysis results in several interesting findings including: (i) the best OSR method to use is heavily dependent on the degree of semantic shift present and (ii) hierarchical rep- resentation learning can improve coarse-grained OSR but has little effect on fine-grained OSR performance. To fur- ther enhance fine-grained OSR performance we propose a hierarchy-adversarial learning method to discourage hier- archical structure in the representation space which results in a perhaps counter-intuitive behaviour and a relative im- provement in fine-grained OSR of up to 2% in AUROC and 7% in AUPR over standard training. Code and data are available: langnico.github.io/fine-grained-osr.