  Despite the impressive performance achieved by data-fusion networks with
duplex encoders for visual semantic segmentation, they become ineffective when
spatial geometric data are not available. Implicitly infusing the spatial
geometric prior knowledge acquired by a duplex-encoder teacher model into a
single-encoder student model is a practical, albeit less explored research
avenue. This paper delves into this topic and resorts to knowledge distillation
approaches to address this problem. We introduce the Learning to Infuse "X"
(LIX) framework, with novel contributions in both logit distillation and
feature distillation aspects. We present a mathematical proof that underscores
the limitation of using a single fixed weight in decoupled knowledge
distillation and introduce a logit-wise dynamic weight controller as a solution
to this issue. Furthermore, we develop an adaptively-recalibrated feature
distillation algorithm, including two technical novelties: feature
recalibration via kernel regression and in-depth feature consistency
quantification via centered kernel alignment. Extensive experiments conducted
with intermediate-fusion and late-fusion networks across various public
datasets provide both quantitative and qualitative evaluations, demonstrating
the superior performance of our LIX framework when compared to other
state-of-the-art approaches.
