  Existing open-set recognition (OSR) studies typically assume that each image
contains only one class label, and the unknown test set (negative) has a
disjoint label space from the known test set (positive), a scenario termed
full-label shift. This paper introduces the mixed OSR problem, where test
images contain multiple class semantics, with known and unknown classes
co-occurring in negatives, leading to a more challenging super-label shift.
Addressing the mixed OSR requires classification models to accurately
distinguish different class semantics within images and measure their
"knowness". In this study, we propose the OpenSlot framework, built upon
object-centric learning. OpenSlot utilizes slot features to represent diverse
class semantics and produce class predictions. Through our proposed
anti-noise-slot (ANS) technique, we mitigate the impact of noise (invalid and
background) slots during classification training, effectively addressing the
semantic misalignment between class predictions and the ground truth. We
conduct extensive experiments with OpenSlot on mixed & conventional OSR
benchmarks. Without elaborate designs, OpenSlot not only exceeds existing OSR
studies in detecting super-label shifts across single & multi-label mixed OSR
tasks but also achieves state-of-the-art performance on conventional
benchmarks. Remarkably, our method can localize class objects without using
bounding boxes during training. The competitive performance in open-set object
detection demonstrates OpenSlot's ability to explicitly explain label shifts
and benefits in computational efficiency and generalization.
