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4D Contrastive Superflows are Dense 3D Representation Learners

Autor: Xu, Xiang, Kong, Lingdong, Shuai, Hui, Zhang, Wenwei, Pan, Liang, Chen, Kai, Liu, Ziwei, Liu, Qingshan

In the realm of autonomous driving, accurate 3D perception is the foundation. However, developing such models relies on extensive human annotations -- a process that is both costly and labor-intensive. To address this challenge from a data representa

Externí odkaz: http://arxiv.org/abs/2407.06190

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Cooperative Sentiment Agents for Multimodal Sentiment Analysis

Autor: Wang, Shanmin, Shuai, Hui, Liu, Qingshan, Wang, Fei

In this paper, we propose a new Multimodal Representation Learning (MRL) method for Multimodal Sentiment Analysis (MSA), which facilitates the adaptive interaction between modalities through Cooperative Sentiment Agents, named Co-SA. Co-SA comprises

Externí odkaz: http://arxiv.org/abs/2404.12642

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FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation

Autor: Xu, Xiang, Kong, Lingdong, Shuai, Hui, Liu, Qingshan

LiDAR segmentation has become a crucial component in advanced autonomous driving systems. Recent range-view LiDAR segmentation approaches show promise for real-time processing. However, they inevitably suffer from corrupted contextual information and

Externí odkaz: http://arxiv.org/abs/2312.04484

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Adaptive Multi-view and Temporal Fusing Transformer for 3D Human Pose Estimation

Autor: Shuai, Hui, Wu, Lele, Liu, Qingshan

This paper proposes a unified framework dubbed Multi-view and Temporal Fusing Transformer (MTF-Transformer) to adaptively handle varying view numbers and video length without camera calibration in 3D Human Pose Estimation (HPE). It consists of Featur

Externí odkaz: http://arxiv.org/abs/2110.05092

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