Recovering detailed geometry from high-resolution images is critical for precise perception of the surroundings and objects. However, existing methods which use latent-space modeling and VAE reconstruction can compromise geometric details. Furthermore, decoding from latent codes introduces substantial inference overhea...
Bo-Wen Chai, Tian-Bao Zhang, Shu-Yu Wu et al.· 0 citations
Multi-vehicle cooperative autonomous driving enhances the safety and reliability of autonomous driving systems through information sharing among connected vehicles, demonstrating significant potential for improving traffic safety. LLM-based approaches leverage strong reasoning capabilities of LLMs to enable effective i...
Zhe Huang, Zhaoxin Fan, Shuo Wang et al.· IEEE transactions on multime...· 0 citations
Cascade is proposed, a hierarchical recoverability control framework that minimizes the internal identifiability of target knowledge and effectively reduces recoverability while maintaining stable model utility.
Qing-Chen Yu, Shi-Ying Duan, Xiao-Dong Li et al.· 0 citations
This paper is the first systematic study of whether prompt-token hidden states in contemporary LLMs exhibit Gromov Hyperbolicity (GH), a distance-based measure of tree-likeness.
Zhi-Chao Yang, Yuanze Hu, Gen Li et al.· 0 citations
LiteMVS is a lightweight multi-view depth estimation model that integrates plane-sweep geometric reasoning with strong monocular semantic and structural priors and employs a Mixture-of-Experts (MoE) formulation to enable adaptive geometric aggregation across depth hypotheses.
Tian-Bao Zhang, Zeyu Liu, Shuyu Wu et al.· 0 citations
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