  Due to limitations in data quality, some essential visual tasks are difficult
to perform independently. Introducing previously unavailable information to
transfer informative dark knowledge has been a common way to solve such hard
tasks. However, research on why transferred knowledge works has not been
extensively explored. To address this issue, in this paper, we discover the
correlation between feature discriminability and dimensional structure (DS) by
analyzing and observing features extracted from simple and hard tasks. On this
basis, we express DS using deep channel-wise correlation and intermediate
spatial distribution, and propose a novel cross-modal knowledge distillation
(CMKD) method for better supervised cross-modal learning (CML) performance. The
proposed method enforces output features to be channel-wise independent and
intermediate ones to be uniformly distributed, thereby learning semantically
irrelevant features from the hard task to boost its accuracy. This is
especially useful in specific applications where the performance gap between
dual modalities is relatively large. Furthermore, we collect a real-world CML
dataset to promote community development. The dataset contains more than 10,000
paired optical and radar images and is continuously being updated. Experimental
results on real-world and benchmark datasets validate the effectiveness of the
proposed method.
