Zobrazeno 1 - 10
of 3 201
pro vyhledávání: '"Li,Duo"'
Autor:
Hu, Jia, Lian, Zhexi, Wang, Haoran, Zhang, Zihan, Qian, Ruoxi, Li, Duo, Jaehyun, So, Zheng, Junnian
The current Adaptive Cruise Control (ACC) systems are vulnerable to "road bully" such as cut-ins. This paper proposed an Anti-bullying Adaptive Cruise Control (AACC) approach with proactive right-of-way protection ability. It bears the following feat
Externí odkaz:
http://arxiv.org/abs/2412.12197
We show that there exist only constant morphisms from $\mathbb{Q}^{2n+1}(n\geq 1)$ to $\mathbb{G}(l,2n+1)$ if $l$ is even $(0
Externí odkaz:
http://arxiv.org/abs/2409.02365
Autor:
Pu, Yifan, Xia, Zhuofan, Guo, Jiayi, Han, Dongchen, Li, Qixiu, Li, Duo, Yuan, Yuhui, Li, Ji, Han, Yizeng, Song, Shiji, Huang, Gao, Li, Xiu
This paper identifies significant redundancy in the query-key interactions within self-attention mechanisms of diffusion transformer models, particularly during the early stages of denoising diffusion steps. In response to this observation, we presen
Externí odkaz:
http://arxiv.org/abs/2408.05710
Personalization is crucial for the widespread adoption of advanced driver assistance system. To match up with each user's preference, the online evolution capability is a must. However, conventional evolution methods learn from naturalistic driving d
Externí odkaz:
http://arxiv.org/abs/2405.07543
To tackle the issues of catastrophic forgetting and overfitting in few-shot class-incremental learning (FSCIL), previous work has primarily concentrated on preserving the memory of old knowledge during the incremental phase. The role of pre-trained m
Externí odkaz:
http://arxiv.org/abs/2402.01201
For semi-supervised learning with imbalance classes, the long-tailed distribution of data will increase the model prediction bias toward dominant classes, undermining performance on less frequent classes. Existing methods also face challenges in ensu
Externí odkaz:
http://arxiv.org/abs/2401.04435
Let $E$ be a uniform bundle on an arbitrary generalised Grassmannian $X$ defined over $\mathbb{C}$. We show that if the rank of $E$ is at most $e.d.(\mathrm{VMRT})$, then $E$ necessarily splits. For some generalised Grassmannians, we prove that the u
Externí odkaz:
http://arxiv.org/abs/2312.04852
Expandable networks have demonstrated their advantages in dealing with catastrophic forgetting problem in incremental learning. Considering that different tasks may need different structures, recent methods design dynamic structures adapted to differ
Externí odkaz:
http://arxiv.org/abs/2207.06754
Autor:
Li, Ziteng, Kang, Shuhao, Liu, Huan, Liu, Yuhu, Ren, Mingjun, Zhang, Xinquan, Zhu, Limin, Li, Duo
Publikováno v:
In Journal of Manufacturing Processes 12 December 2024 131:2505-2513
