  Mainstream object detectors are commonly constituted of two sub-tasks,
including classification and regression tasks, implemented by two parallel
heads. This classic design paradigm inevitably leads to inconsistent spatial
distributions between classification score and localization quality (IOU).
Therefore, this paper alleviates this misalignment in the view of knowledge
distillation. First, we observe that the massive teacher achieves a higher
proportion of harmonious predictions than the lightweight student. Based on
this intriguing observation, a novel Harmony Score (HS) is devised to estimate
the alignment of classification and regression qualities. HS models the
relationship between two sub-tasks and is seen as prior knowledge to promote
harmonious predictions for the student. Second, this spatial misalignment will
result in inharmonious region selection when distilling features. To alleviate
this problem, a novel Task-decoupled Feature Distillation (TFD) is proposed by
flexibly balancing the contributions of classification and regression tasks.
Eventually, HD and TFD constitute the proposed method, named Task-Balanced
Distillation (TBD). Extensive experiments demonstrate the considerable
potential and generalization of the proposed method. Specifically, when
equipped with TBD, RetinaNet with ResNet-50 achieves 41.0 mAP under the COCO
benchmark, outperforming the recent FGD and FRS.
