2 code implementations • 8 Apr 2024 • Yurong You, Cheng Perng Phoo, Carlos Andres Diaz-Ruiz, Katie Z Luo, Wei-Lun Chao, Mark Campbell, Bharath Hariharan, Kilian Q Weinberger
Accurate 3D object detection is crucial to autonomous driving.
1 code implementation • 5 Jan 2024 • Jiawei Yang, Katie Z Luo, Jiefeng Li, Kilian Q Weinberger, Yonglong Tian, Yue Wang
Our two-stage approach, termed Denoising Vision Transformers (DVT), does not require re-training existing pre-trained ViTs and is immediately applicable to any Transformer-based architecture.
1 code implementation • 7 Nov 2023 • Katie Z Luo, Xinshuo Weng, Yan Wang, Shuang Wu, Jie Li, Kilian Q Weinberger, Yue Wang, Marco Pavone
We propose a novel framework to integrate SD maps into online map prediction and propose a Transformer-based encoder, SD Map Encoder Representations from transFormers, to leverage priors in SD maps for the lane-topology prediction task.
1 code implementation • 27 Mar 2023 • Xiangyu Chen, Varsha Kishore, Kilian Q Weinberger
Image steganography is the process of concealing secret information in images through imperceptible changes.
no code implementations • 23 Sep 2022 • Youya Xia, Josephine Monica, Wei-Lun Chao, Bharath Hariharan, Kilian Q Weinberger, Mark Campbell
In this paper, we investigate the idea of turning sensor inputs (i. e., images) captured in an adverse condition into a benign one (i. e., sunny), upon which the downstream tasks (e. g., semantic segmentation) can attain high accuracy.
1 code implementation • ICLR 2022 • Varsha Kishore, Xiangyu Chen, Yan Wang, Boyi Li, Kilian Q Weinberger
Recent attempts at image steganography make use of advances in deep learning to train an encoder-decoder network pair to hide and retrieve secret messages in images.
no code implementations • 26 Mar 2021 • Yurong You, Carlos Andres Diaz-Ruiz, Yan Wang, Wei-Lun Chao, Bharath Hariharan, Mark Campbell, Kilian Q Weinberger
Self-driving cars must detect other vehicles and pedestrians in 3D to plan safe routes and avoid collisions.