Existing adaptive reasoning methods for driving Vision-Language-Action (VLA) models primarily focus on whether to reason, overlooking how reasoning should differ across driving situations. Our key insight is that while scene complexity informs reasoning depth, dynamic risk is equally critical for deciding how to reason...
Xiao-Lei Chen, Zhuo-Lin He, Yuxuan Liang et al.· 0 citations
In few-shot industrial anomaly detection, the few normal target images provide no direct defect supervision, making anomaly prompts difficult to learn from these samples alone. Some vision-language methods therefore use manually specified descriptions to supply explicit anomaly semantics. However, constructing these de...
Meng-Yang Zhao, Teng Fu, Hai-Yang Yu et al.· 0 citations
This work proposes Visual Difference DeepStack (VD-DeepStack), which explicitly conditions language reasoning on query-reference visual differences on query-reference visual differences, and supports mitigating the visual comparison-reasoning gap through the joint design of comparison representations and their integrat...
Meng-Yang Zhao, Zhuo-Lin He, Hai-Yang Yu et al.· 1 citation
CRISP is proposed, a pre-LLM yet text-driven visual token pruning framework that preserves both instruction-relevant evidence and essential scene context that serves as a practical solution for efficient LVLM inference, especially in resource-constrained scenarios.
Xu Li, Yi Zheng, Mengyang Zhao et al.· arXiv.org· 1 citation
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