3 accepted papers
Lane-level maps are increasingly preferred over Standard-Definition (SD) and High-Definition (HD) maps, offering a better trade-off among detail richness, coverage breadth, and data freshness. However, constructing city-scale lane-level maps remains time-consuming and labor-intensive. To address the
Heterogeneous Large Language Model (LLM) fusion integrates the strengths of multiple source LLMs with different architectures into a target LLM with low computational overhead. While promising, existing methods suffer from two major limitations: 1) **reliance on real data from limited domain** for k…
The transferability of adversarial perturbations provides an effective shortcut for black-box attacks. Targeted perturbations have greater practicality but are more difficult to transfer between models. In this paper, we experimentally and theoretically demonstrated that neural networks trained on t…