Zobrazeno 1 - 10
of 70
pro vyhledávání: '"Cottereau, Benoît"'
Autor:
Kong, Lingdong, Xie, Shaoyuan, Hu, Hanjiang, Niu, Yaru, Ooi, Wei Tsang, Cottereau, Benoit R., Ng, Lai Xing, Ma, Yuexin, Zhang, Wenwei, Pan, Liang, Chen, Kai, Liu, Ziwei, Qiu, Weichao, Zhang, Wei, Cao, Xu, Lu, Hao, Chen, Ying-Cong, Kang, Caixin, Zhou, Xinning, Ying, Chengyang, Shang, Wentao, Wei, Xingxing, Dong, Yinpeng, Yang, Bo, Jiang, Shengyin, Ma, Zeliang, Ji, Dengyi, Li, Haiwen, Huang, Xingliang, Tian, Yu, Kou, Genghua, Jia, Fan, Liu, Yingfei, Wang, Tiancai, Li, Ying, Hao, Xiaoshuai, Yang, Yifan, Zhang, Hui, Wei, Mengchuan, Zhou, Yi, Zhao, Haimei, Zhang, Jing, Li, Jinke, He, Xiao, Cheng, Xiaoqiang, Zhang, Bingyang, Zhao, Lirong, Ding, Dianlei, Liu, Fangsheng, Yan, Yixiang, Wang, Hongming, Ye, Nanfei, Luo, Lun, Tian, Yubo, Zuo, Yiwei, Cao, Zhe, Ren, Yi, Li, Yunfan, Liu, Wenjie, Wu, Xun, Mao, Yifan, Li, Ming, Liu, Jian, Liu, Jiayang, Qin, Zihan, Chu, Cunxi, Xu, Jialei, Zhao, Wenbo, Jiang, Junjun, Liu, Xianming, Wang, Ziyan, Li, Chiwei, Li, Shilong, Yuan, Chendong, Yang, Songyue, Liu, Wentao, Chen, Peng, Zhou, Bin, Wang, Yubo, Zhang, Chi, Sun, Jianhang, Chen, Hai, Yang, Xiao, Wang, Lizhong, Fu, Dongyi, Lin, Yongchun, Yang, Huitong, Li, Haoang, Luo, Yadan, Cheng, Xianjing, Xu, Yong
In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles. Challenges such as adverse weather, sensor malfunctions, and environmental unpredictability can severely impa
Externí odkaz:
http://arxiv.org/abs/2405.08816
Event-based semantic segmentation (ESS) is a fundamental yet challenging task for event camera sensing. The difficulties in interpreting and annotating event data limit its scalability. While domain adaptation from images to event data can help to mi
Externí odkaz:
http://arxiv.org/abs/2405.05259
Autor:
Kong, Lingdong, Xie, Shaoyuan, Hu, Hanjiang, Ng, Lai Xing, Cottereau, Benoit R., Ooi, Wei Tsang
Depth estimation from monocular images is pivotal for real-world visual perception systems. While current learning-based depth estimation models train and test on meticulously curated data, they often overlook out-of-distribution (OoD) situations. Ye
Externí odkaz:
http://arxiv.org/abs/2310.15171
Autor:
Kong, Lingdong, Niu, Yaru, Xie, Shaoyuan, Hu, Hanjiang, Ng, Lai Xing, Cottereau, Benoit R., Zhang, Liangjun, Wang, Hesheng, Ooi, Wei Tsang, Zhu, Ruijie, Song, Ziyang, Liu, Li, Zhang, Tianzhu, Yu, Jun, Jing, Mohan, Li, Pengwei, Qi, Xiaohua, Jin, Cheng, Chen, Yingfeng, Hou, Jie, Zhang, Jie, Kan, Zhen, Ling, Qiang, Peng, Liang, Li, Minglei, Xu, Di, Yang, Changpeng, Yao, Yuanqi, Wu, Gang, Kuai, Jian, Liu, Xianming, Jiang, Junjun, Huang, Jiamian, Li, Baojun, Chen, Jiale, Zhang, Shuang, Ao, Sun, Li, Zhenyu, Chen, Runze, Luo, Haiyong, Zhao, Fang, Yu, Jingze
Accurate depth estimation under out-of-distribution (OoD) scenarios, such as adverse weather conditions, sensor failure, and noise contamination, is desirable for safety-critical applications. Existing depth estimation systems, however, suffer inevit
Externí odkaz:
http://arxiv.org/abs/2307.15061
Autor:
Cuadrado, Javier, Rançon, Ulysse, Cottereau, Benoît, Barranco, Francisco, Masquelier, Timothée
Event-based cameras are raising interest within the computer vision community. These sensors operate with asynchronous pixels, emitting events, or "spikes", when the luminance change at a given pixel since the last event surpasses a certain threshold
Externí odkaz:
http://arxiv.org/abs/2302.06492
Deep neural networks have surpassed human performance in key visual challenges such as object recognition, but require a large amount of energy, computation, and memory. In contrast, spiking neural networks (SNNs) have the potential to improve both t
Externí odkaz:
http://arxiv.org/abs/2212.00081
Deep neural networks have surpassed human performance in key visual challenges such as object recognition, but require a large amount of energy, computation, and memory. In contrast, spiking neural networks (SNNs) have the potential to improve both t
Externí odkaz:
http://arxiv.org/abs/2205.10338
Depth estimation is an important computer vision task, useful in particular for navigation in autonomous vehicles, or for object manipulation in robotics. Here we solved it using an end-to-end neuromorphic approach, combining two event-based cameras
Externí odkaz:
http://arxiv.org/abs/2109.13751
Autor:
Laurent, Marie-Alphée, Audurier, Pauline, De Castro, Vanessa, Gao, Xiaoqing, Durand, Jean-Baptiste, Jonas, Jacques, Rossion, Bruno, Cottereau, Benoit R.
Publikováno v:
In NeuroImage 15 April 2023 270
Akademický článek
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