106 citations · 565 across the 31 of their papers we have counts for
7 papers · 1 filter
Distribution-Aware Calibration for Object Detection with Noisy Bounding Boxes
Donghao Zhou, Jialin Li, Jinpeng Li +7
Large-scale well-annotated datasets are of great importance for training an effective object detector. However, obtaining accurate bounding box annotations is laborious and demandi…
DPPMask: Masked Image Modeling with Determinantal Point Processes
Junde Xu, Zikai Lin, Donghao Zhou +5
Masked Image Modeling (MIM) has achieved impressive representative performance with the aim of reconstructing randomly masked images. Despite the empirical success, most previous w…
Traj-MAE: Masked Autoencoders for Trajectory Prediction
Hao Chen, Jiaze Wang, Kun Shao +5
Trajectory prediction has been a crucial task in building a reliable autonomous driving system by anticipating possible dangers. One key issue is to generate consistent trajectory…
PointPatchMix: Point Cloud Mixing with Patch Scoring
Yi Wang, Jiaze Wang, Jinpeng Li +4
Data augmentation is an effective regularization strategy for mitigating overfitting in deep neural networks, and it plays a crucial role in 3D vision tasks, where the point cloud…
DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and Reconstruction
Bowen Wang, Chen Liang, Jiaze Wang +7
Attaining the equilibrium state of a catalyst-adsorbate system is key to fundamentally assessing its effective properties, such as adsorption energy. Machine learning methods with…
Adaptive Negative Evidential Deep Learning for Open-set Semi-supervised Learning
Yang Yu, Danruo Deng, Furui Liu +4
Semi-supervised learning (SSL) methods assume that labeled data, unlabeled data and test data are from the same distribution. Open-set semi-supervised learning (Open-set SSL) consi…
Uncertainty Estimation by Fisher Information-based Evidential Deep Learning
Danruo Deng, Guangyong Chen, Yang Yu +2
Uncertainty estimation is a key factor that makes deep learning reliable in practical applications. Recently proposed evidential neural networks explicitly account for different un…