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Learning-to-Defer for Extractive Question Answering

Autor: Montreuil, Yannis, Carlier, Axel, Ng, Lai Xing, Ooi, Wei Tsang

Pre-trained language models have profoundly impacted the field of extractive question-answering, leveraging large-scale textual corpora to enhance contextual language understanding. Despite their success, these models struggle in complex scenarios th

Externí odkaz: http://arxiv.org/abs/2410.15761

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Two-stage Learning-to-Defer for Multi-Task Learning

Autor: Montreuil, Yannis, Yeo, Shu Heng, Carlier, Axel, Ng, Lai Xing, Ooi, Wei Tsang

The Learning-to-Defer approach has been explored for classification and, more recently, regression tasks separately. Many contemporary learning tasks, however, involves both classification and regression components. In this paper, we introduce a Lear

Externí odkaz: http://arxiv.org/abs/2410.15729

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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

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OpenESS: Event-based Semantic Scene Understanding with Open Vocabularies

Autor: Kong, Lingdong, Liu, Youquan, Ng, Lai Xing, Cottereau, Benoit R., Ooi, Wei Tsang

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

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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

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The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation

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

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