  The recently proposed open-world object and open-set detection have achieved
a breakthrough in finding never-seen-before objects and distinguishing them
from known ones. However, their studies on knowledge transfer from known
classes to unknown ones are not deep enough, resulting in the scanty capability
for detecting unknowns hidden in the background. In this paper, we propose the
unknown sniffer (UnSniffer) to find both unknown and known objects. Firstly,
the generalized object confidence (GOC) score is introduced, which only uses
known samples for supervision and avoids improper suppression of unknowns in
the background. Significantly, such confidence score learned from known objects
can be generalized to unknown ones. Additionally, we propose a negative energy
suppression loss to further suppress the non-object samples in the background.
Next, the best box of each unknown is hard to obtain during inference due to
lacking their semantic information in training. To solve this issue, we
introduce a graph-based determination scheme to replace hand-designed
non-maximum suppression (NMS) post-processing. Finally, we present the Unknown
Object Detection Benchmark, the first publicly benchmark that encompasses
precision evaluation for unknown detection to our knowledge. Experiments show
that our method is far better than the existing state-of-the-art methods.
