In open-set recognition (OSR), a promising strategy is exploiting pseudo-unknown data outside given $K$ known classes as an additional $K$+$1$-th class to explicitly model potential open space. However, treating unknown classes without distinction is unequal for them relative to known classes due to the category-agnostic and scale-agnostic of the unknowns. This inevitably not only disrupts the inherent distributions of unknown classes but also incurs both class-wise and instance-wise imbalances between known and unknown classes. Ideally, the OSR problem should model the whole class space as $K$+$\infty$, but enumerating all unknowns is impractical. Since the core of OSR is to effectively model the boundaries of known classes, this means just focusing on the unknowns nearing the boundaries of targeted known classes seems sufficient. Thus, as a compromise, we convert the open classes from infinite to $K$, with a novel concept Target-Aware Universum (TAU) and propose a simple yet effective framework Dual Contrastive Learning with Target-Aware Universum (DCTAU). In details, guided by the targeted known classes, TAU automatically expands the unknown classes from the previous $1$ to $K$, effectively alleviating the distribution disruption and the imbalance issues mentioned above. Then, a novel Dual Contrastive (DC) loss is designed, where all instances irrespective of known or TAU are considered as positives to contrast with their respective negatives. Experimental results indicate DCTAU sets a new state-of-the-art.

翻译：在开放集识别（OSR）中，一种有前景的策略是利用给定K个已知类别之外的伪未知数据，作为额外的第K+1类来显式建模潜在的开放空间。然而，由于未知类别的类别无关性和尺度无关性，对未知类别不加区分地处理会使其相对于已知类别处于不平等地位。这不仅不可避免地破坏了未知类别的内在分布，还会在已知与未知类别之间引发类别级和实例级的不平衡。理想情况下，OSR问题应将整个类别空间建模为K+∞，但枚举所有未知类别是不切实际的。由于OSR的核心在于有效建模已知类别的边界，这意味着仅关注临近目标已知类别边界的未知类别似乎就足够了。因此，作为一种折衷方案，我们通过引入新颖的"目标感知通用集（TAU）"概念，将开放类别从无限转化为K个，并提出一种简单而有效的框架——基于目标感知通用集的双重对比学习（DCTAU）。具体而言，在目标已知类别的引导下，TAU自动将未知类别从先前的1个扩展至K个，有效缓解了上述分布破坏与不平衡问题。随后，我们设计了一种新型的双重对比（DC）损失函数，其中所有实例（无论是已知类还是TAU类）均被视为正样本，与其对应的负样本进行对比。实验结果表明，DCTAU达到了新的最优性能水平。