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The recently proposed open-world object and open-set detection have achieveda breakthrough in finding never-seen-before objects and distinguishing themfrom known ones. However, their studies on knowledge transfer from knownclasses to unknown ones are not deep enough, resulting in the scanty capabilityfor detecting unknowns hidden in the background. In this paper, we propose theunknown sniffer (UnSniffer) to find both unknown and known objects. Firstly,the generalized object confidence (GOC) score is introduced, which only usesknown samples for supervision and avoids improper suppression of unknowns inthe background. Significantly, such confidence score learned from known objectscan be generalized to unknown ones. Additionally, we propose a negative energysuppression 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 tolacking their semantic information in training. To solve this issue, weintroduce a graph-based determination scheme to replace hand-designednon-maximum suppression (NMS) post-processing. Finally, we present the UnknownObject Detection Benchmark, the first publicly benchmark that encompassesprecision evaluation for unknown detection to our knowledge. Experiments showthat our method is far better than the existing state-of-the-art methods.

Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown Objects