  A well-trained model should classify objects with a unanimous score for every
category. This requires the high-level semantic features should be as much
alike as possible among samples. To achive this, previous works focus on
re-designing the loss or proposing new regularization constraints. In this
paper, we provide a new perspective. For each category, it is assumed that
there are two feature sets: one with reliable information and the other with
less reliable source. We argue that the reliable set could guide the feature
learning of the less reliable set during training - in spirit of student
mimicking teacher behavior and thus pushing towards a more compact class
centroid in the feature space. Such a scheme also benefits the reliable set
since samples become closer within the same category - implying that it is
easier for the classifier to identify. We refer to this mutual learning process
as feature intertwiner and embed it into object detection. It is well-known
that objects of low resolution are more difficult to detect due to the loss of
detailed information during network forward pass (e.g., RoI operation). We thus
regard objects of high resolution as the reliable set and objects of low
resolution as the less reliable set. Specifically, an intertwiner is designed
to minimize the distribution divergence between two sets. The choice of
generating an effective feature representation for the reliable set is further
investigated, where we introduce the optimal transport (OT) theory into the
framework. Samples in the less reliable set are better aligned with aid of OT
metric. Incorporated with such a plug-and-play intertwiner, we achieve an
evident improvement over previous state-of-the-arts.
