5 accepted papers
Dynamic recommendation systems aim to provide personalized suggestions by modeling temporal user-item interactions across time-series behavioral data. Recent studies have leveraged pre-trained dynamic graph neural networks (GNNs) to learn user-item representations over temporal snapshot graphs. Howe
LiDAR-based 3D semantic segmentation is a critical task in autonomous driving, but its scalability is limited by the reliance on large-scale labeled datasets. Semi-supervised learning (SSL) offers a potential solution by leveraging unlabeled data. However, most existing SSL segmentation methods are …
Multi-sensor fusion is a key technology in the field of autonomous driving and robotics. Traditional offline multi-sensor fusion calibration methods rely on manual operations and fail to meet real-time requirements, while recent online calibration technologies have limited generalization capabilitie
This research proposes a novel semi-supervised learning framework FourStr (Four-Stream formed by two two-stream models) that focuses on the improvement of fusion and labeling efficiency for 3D multi-sensor detector. FourStr adopts a multi-sensor single-stage detector named adaptive fusion network (A…
The feature pyramid, which is a vital component of the convolutional neural networks, plays a significant role in several perception tasks, including object detection for autonomous driving. However, how to better fuse multi-level and multi-sensor feature pyramids is still a significant challenge, e…