Core Concepts
Implicitly infusing spatial geometric prior knowledge enhances visual semantic segmentation for autonomous driving.
Abstract
This paper introduces the Learning to Infuse “X” (LIX) framework, focusing on logit and feature distillation to improve performance. Extensive experiments show superior results compared to state-of-the-art approaches across various datasets. Key contributions include dynamically-weighted logit distillation and adaptively-recalibrated feature distillation algorithms.
Data-fusion networks with duplex encoders outperform single-modal networks in visual semantic segmentation.
Limitations of data-fusion networks without spatial geometric data availability.
Introduction of LIX framework for implicit infusion of spatial geometric prior knowledge.
Novel contributions in logit and feature distillation aspects.
Mathematical proof highlighting limitations of fixed weights in decoupled knowledge distillation.
Adaptive recalibration approach based on kernel regression for feature consistency quantification.
Superior performance demonstrated through quantitative and qualitative evaluations on public datasets.
Stats
この論文は、データ融合ネットワークの限界や新しいアプローチであるLIXフレームワークに焦点を当てています。