most citedPVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformer

4 citations · 9 across the 5 of their papers we have counts for

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cs.CV20241 cited

NeRF-Det++: Incorporating Semantic Cues and Perspective-aware Depth Supervision for Indoor Multi-View 3D Detection

Chenxi Huang, Yuenan Hou, Weicai Ye +5

NeRF-Det has achieved impressive performance in indoor multi-view 3D detection by innovatively utilizing NeRF to enhance representation learning. Despite its notable performance, w…

cs.CV2023

A Study of Unsupervised Evaluation Metrics for Practical and Automatic Domain Adaptation

Minghao Chen, Zepeng Gao, Shuai Zhao +4

Unsupervised domain adaptation (UDA) methods facilitate the transfer of models to target domains without labels. However, these methods necessitate a labeled target validation set…

cs.CV20237 cited

NormKD: Normalized Logits for Knowledge Distillation

Zhihao Chi, Tu Zheng, Hengjia Li +4

Logit based knowledge distillation gets less attention in recent years since feature based methods perform better in most cases. Nevertheless, we find it still has untapped potenti…

cs.CV20234 cited

PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformer

Honghui Yang, Wenxiao Wang, Minghao Chen +5

Recent Transformer-based 3D object detectors learn point cloud features either from point- or voxel-based representations. However, the former requires time-consuming sampling whil…

cs.CV20234 cited

APPT : Asymmetric Parallel Point Transformer for 3D Point Cloud Understanding

Hengjia Li, Tu Zheng, Zhihao Chi +5

Transformer-based networks have achieved impressive performance in 3D point cloud understanding. However, most of them concentrate on aggregating local features, but neglect to dir…

cs.CV2022

Towards In-distribution Compatibility in Out-of-distribution Detection

Boxi Wu, Jie Jiang, Haidong Ren +7

Deep neural network, despite its remarkable capability of discriminating targeted in-distribution samples, shows poor performance on detecting anomalous out-of-distribution data. T…