no code implementations • 19 Apr 2024 • Tianyuan Zhang, Hong-Xing Yu, Rundi Wu, Brandon Y. Feng, Changxi Zheng, Noah Snavely, Jiajun Wu, William T. Freeman
Unlike unconditional or text-conditioned dynamics generation, action-conditioned dynamics requires perceiving the physical material properties of objects and grounding the 3D motion prediction on these properties, such as object stiffness.
no code implementations • 7 Aug 2023 • Wei Jiang, Tianyuan Zhang, Shuangcheng Liu, Weiyu Ji, Zichao Zhang, Gang Xiao
Through this pipeline, we establish the Discrete and Continuous Instant-level (DCI) dataset, enabling comprehensive experiments involving three detection models and three physical adversarial attacks.
no code implementations • 4 Aug 2023 • Yisong Xiao, Aishan Liu, Tianyuan Zhang, Haotong Qin, Jinyang Guo, Xianglong Liu
Quantization has emerged as an essential technique for deploying deep neural networks (DNNs) on devices with limited resources.
no code implementations • 11 Apr 2023 • Tianyuan Zhang, Yisong Xiao, Xiaoya Zhang, Hao Li, Lu Wang
Thus, virtual simulation experiments can provide a solution to this challenge.
no code implementations • 8 Apr 2023 • Yisong Xiao, Tianyuan Zhang, Shunchang Liu, Haotong Qin
To address this gap, we thoroughly evaluated the robustness of quantized models against various noises (adversarial attacks, natural corruptions, and systematic noises) on ImageNet.
no code implementations • CVPR 2023 • Tianyuan Zhang, Mark Sheinin, Dorian Chan, Mark Rau, Matthew O’Toole, Srinivasa G. Narasimhan
The subtle vibrations on an object's surface contain information about the object's physical properties and its interaction with the environment.
1 code implementation • CVPR 2023 • Junru Gu, Chenxu Hu, Tianyuan Zhang, Xuanyao Chen, Yilun Wang, Yue Wang, Hang Zhao
In this work, we propose ViP3D, a query-based visual trajectory prediction pipeline that exploits rich information from raw videos to directly predict future trajectories of agents in a scene.
1 code implementation • 2 May 2022 • Tianyuan Zhang, Xuanyao Chen, Yue Wang, Yilun Wang, Hang Zhao
In contrast to prior works, MUTR3D does not explicitly rely on the spatial and appearance similarity of objects.
1 code implementation • 20 Mar 2022 • Xuanyao Chen, Tianyuan Zhang, Yue Wang, Yilun Wang, Hang Zhao
Sensor fusion is an essential topic in many perception systems, such as autonomous driving and robotics.
2 code implementations • CVPR 2022 • Lue Fan, Ziqi Pang, Tianyuan Zhang, Yu-Xiong Wang, Hang Zhao, Feng Wang, Naiyan Wang, Zhaoxiang Zhang
In LiDAR-based 3D object detection for autonomous driving, the ratio of the object size to input scene size is significantly smaller compared to 2D detection cases.
Ranked #3 on 3D Object Detection on waymo cyclist
1 code implementation • 13 Oct 2021 • Yue Wang, Vitor Guizilini, Tianyuan Zhang, Yilun Wang, Hang Zhao, Justin Solomon
This top-down approach outperforms its bottom-up counterpart in which object bounding box prediction follows per-pixel depth estimation, since it does not suffer from the compounding error introduced by a depth prediction model.
Ranked #7 on Robust Camera Only 3D Object Detection on nuScenes-C
12 code implementations • 12 Nov 2020 • Zhewei Huang, Tianyuan Zhang, Wen Heng, Boxin Shi, Shuchang Zhou
We propose RIFE, a Real-time Intermediate Flow Estimation algorithm for Video Frame Interpolation (VFI).
no code implementations • 26 Nov 2019 • Tianyuan Zhang, Bichen Wu, Xin Wang, Joseph Gonzalez, Kurt Keutzer
In this work, we propose a method to improve the model capacity without increasing inference-time complexity.
1 code implementation • 23 May 2019 • Tianyuan Zhang, Zhanxing Zhu
Our findings shed some light on why AT-CNNs are more robust than those normally trained ones and contribute to a better understanding of adversarial training over CNNs from an interpretation perspective.
2 code implementations • NeurIPS 2019 • Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong
Adversarial training, typically formulated as a robust optimization problem, is an effective way of improving the robustness of deep networks.