75 citations · 414 across the 53 of their papers we have counts for
25 papers · 1 filter
ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target Simulation
Bo Zhang, Xinyu Cai, Jiakang Yuan +10
Domain shifts such as sensor type changes and geographical situation variations are prevalent in Autonomous Driving (AD), which poses a challenge since AD model relying on the prev…
Patch-Level Contrasting without Patch Correspondence for Accurate and Dense Contrastive Representation Learning
Shaofeng Zhang, Feng Zhu, Rui Zhao +1
We propose ADCLR: A ccurate and D ense Contrastive Representation Learning, a novel self-supervised learning framework for learning accurate and dense vision representation. To ext…
Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving
Xiaosong Jia, Penghao Wu, Li Chen +4
End-to-end autonomous driving has made impressive progress in recent years. Existing methods usually adopt the decoupled encoder-decoder paradigm, where the encoder extracts hidden…
Geometric-aware Pretraining for Vision-centric 3D Object Detection
Linyan Huang, Huijie Wang, Jia Zeng +4
Multi-camera 3D object detection for autonomous driving is a challenging problem that has garnered notable attention from both academia and industry. An obstacle encountered in vis…
H2RBox-v2: Incorporating Symmetry for Boosting Horizontal Box Supervised Oriented Object Detection
Yi Yu, Xue Yang, Qingyun Li +4
With the rapidly increasing demand for oriented object detection, e.g. in autonomous driving and remote sensing, the recently proposed paradigm involving weakly-supervised detector…
Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm
Yichen Xie, Han Lu, Junchi Yan +3
Given the large-scale data and the high annotation cost, pretraining-finetuning becomes a popular paradigm in multiple computer vision tasks. Previous research has covered both the…