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We endeavor on a rarely explored task named Insubstantial Object Detection(IOD), which aims to localize the object with following characteristics: (1)amorphous shape with indistinct boundary; (2) similarity to surroundings; (3)absence in color. Accordingly, it is far more challenging to distinguishinsubstantial objects in a single static frame and the collaborativerepresentation of spatial and temporal information is crucial. Thus, weconstruct an IOD-Video dataset comprised of 600 videos (141,017 frames)covering various distances, sizes, visibility, and scenes captured by differentspectral ranges. In addition, we develop a spatio-temporal aggregationframework for IOD, in which different backbones are deployed and aspatio-temporal aggregation loss (STAloss) is elaborately designed to leveragethe consistency along the time axis. Experiments conducted on IOD-Video datasetdemonstrate that spatio-temporal aggregation can significantly improve theperformance of IOD. We hope our work will attract further researches into thisvaluable yet challenging task. The code will be available at:\url{https://github.com/CalayZhou/IOD-Video}.

Explore Spatio-temporal Aggregation for Insubstantial Object Detection: Benchmark Dataset and Baseline