The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition

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

RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions

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

Efficient multi-scale representation of visual objects using a biologically plausible spike-latency code and winner-take-all inhibition

Autor: Sanchez-Garcia, Melani, Chauhan, Tushar, Cottereau, Benoit R., Beyeler, Michael

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

StereoSpike: Depth Learning with a Spiking Neural Network

Autor: Rançon, Ulysse, Cuadrado-Anibarro, Javier, Cottereau, Benoit R., Masquelier, Timothée

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

Processing of translational, radial and rotational optic flow in older adults

Autor: Jade Guénot, Yves Trotter, Angélique Delaval, Robin Baurès, Vincent Soler, Benoit R. Cottereau

Publikováno v: Scientific Reports, Vol 13, Iss 1, Pp 1-11 (2023)

Abstract Aging impacts human observer’s performance in a wide range of visual tasks and notably in motion discrimination. Despite numerous studies, we still poorly understand how optic flow processing is impacted in healthy older adults. Here, we e

Externí odkaz: https://doaj.org/article/9b0f80092bd94c839204b45084b135a0