  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 distinguish
insubstantial objects in a single static frame and the collaborative
representation of spatial and temporal information is crucial. Thus, we
construct an IOD-Video dataset comprised of 600 videos (141,017 frames)
covering various distances, sizes, visibility, and scenes captured by different
spectral ranges. In addition, we develop a spatio-temporal aggregation
framework for IOD, in which different backbones are deployed and a
spatio-temporal aggregation loss (STAloss) is elaborately designed to leverage
the consistency along the time axis. Experiments conducted on IOD-Video dataset
demonstrate that spatio-temporal aggregation can significantly improve the
performance of IOD. We hope our work will attract further researches into this
valuable yet challenging task. The code will be available at:
\url{https://github.com/CalayZhou/IOD-Video}.
