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RSKD: Grounding Semantic Consistency in Driving VLMs via Distillation

2026-10-26 01:25:38
Chuchu Xie, Yang Zhong, Kangan Qian, Hao Ye, Kun Jiang, Diange Yang
arXiv_CV
               
Abstract

Knowledge distillation can transfer large multimodal models to compact models for autonomous driving, but uniform token-level matching can dilute planning-relevant semantics and amplify teacher errors. We propose RSKD, a semantic-consistency-guided distillation framework that routes supervision according to both the task importance of an answer phrase and its semantic consistency with the teacher prediction. Task-critical semantic phrases are localized in the ground-truth answer, and a phrase-level consistency score controls the balance between Jensen--Shannon divergence (JSD) distillation and ground-truth supervision. Teacher information is emphasized only where it is semantically consistent, while semantically inconsistent phrases receive stronger ground-truth supervision. We evaluate RSKD on DrivingVQA, LingoQA, and MapLMv2. RSKD improves over the compared student-based baselines on DrivingVQA and LingoQA and remains competitive on MapLMv2.

Abstract (translated)

URL

https://arxiv.org/abs/2610.34689

PDF

https://arxiv.org/pdf/2610.34689.pdf

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