4 papers indexed
Kang Ding, Zhigui Lin, Hongsong Wang, Jie Gui, Qi Liu, Zhe Wang, et al.
This letter presents PrismAD, a decoupled end-to-end autonomous driving framework based on a Semantic Mixture-of-Planners. Existing planners usually aggregate heterogeneous scene tokens into a coupled representation space, forcing a single planning branch to jointly model agent i…
Jiahui Xiong, Qiuxia Lai, Hongsong Wang
Continual Generalized Category Discovery (C-GCD) aims to incrementally identify novel categories from sequential unlabeled data while preserving recognition of known classes, which is an essential capability for open-world visual learning. A major bottleneck lies in ambiguous unl…
Runzhi Deng, Yundi Hu, Yiming Zhong, Zhao Wang, Xixi Liu, Hongsong Wang, et al.
Large Multimodal Models (LMMs) show strong few-shot generalization, but industrial anomaly detection remains difficult because defects are small, input resolution is limited, and textual standards are not always grounded in visual evidence. Recent optimization-based methods impro…
Qi Liu, Yabei Li, Hongsong Wang, Heng Zhang, Lei He
Autonomous driving planning requires translating navigation intent, traffic rules, dynamic interactions, and language instructions into executable continuous trajectories. Vision-Language-Action models have been introduced into driving planning to improve long-tail generalization…