LIX: 自動運転向け視覚的セマンティックセグメンテーションに空間幾何学的事前知識を暗黙的に注入する手法
LIX: Implicitly Infusing Spatial Geometric Prior Knowledge into Visual Semantic Segmentation for Autonomous Driving
データ融合型教師ネットワークが持つ空間幾何学的事前知識を、単一モーダルの生徒ネットワークに知識蒸留で暗黙的に注入するLIXフレームワークを提案し、ロジット蒸留と特徴蒸留の両面で新手法を導入して性能を向上させた。
著者: Sicen Guo, Ziwei Long, Zhiyuan Wu, Qijun Chen, Ioannis Pitas, Rui Fan
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
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 data-fusion teacher network into a single-modal student network is a practical, albeit less explored research avenue. This article 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 novel techniques: 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.
