no code implementations • 11 Mar 2024 • Xiuwei Xu, Chong Xia, Ziwei Wang, Linqing Zhao, Yueqi Duan, Jie zhou, Jiwen Lu
To this end, we propose an adapter-based plug-and-play module for the backbone of 3D scene perception model, which constructs memory to cache and aggregate the extracted RGB-D features to empower offline models with temporal learning ability.
1 code implementation • NeurIPS 2023 • Yinan Liang, Ziwei Wang, Xiuwei Xu, Yansong Tang, Jie zhou, Jiwen Lu
Due to the high price and heavy energy consumption of GPUs, deploying deep models on IoT devices such as microcontrollers makes significant contributions for ecological AI.
no code implementations • 9 Oct 2023 • Zhenyu Wu, Xiuwei Xu, Ziwei Wang, Chong Xia, Linqing Zhao, Jiwen Lu, Haibin Yan
Existing methods only consider fixed frames of input data for a single detector, such as monocular RGB-D images or point clouds reconstructed from dense multi-view RGB-D images.
1 code implementation • 4 Jul 2023 • Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan
Equipping embodied agents with commonsense is important for robots to successfully complete complex human instructions in general environments.
1 code implementation • 30 May 2023 • Changyuan Wang, Ziwei Wang, Xiuwei Xu, Yansong Tang, Jie zhou, Jiwen Lu
On the contrary, we design group-wise quantization functions for activation discretization in different timesteps and sample the optimal timestep for informative calibration image generation, so that our quantized diffusion model can reduce the discretization errors with negligible computational overhead.
1 code implementation • 5 May 2023 • Xiuwei Xu, Zhihao Sun, Ziwei Wang, Hongmin Liu, Jie zhou, Jiwen Lu
Specifically, we theoretically derive a dynamic spatial pruning (DSP) strategy to prune the redundant spatial representation of 3D scene in a cascade manner according to the distribution of objects.
no code implementations • CVPR 2023 • Xiuwei Xu, Ziwei Wang, Jie zhou, Jiwen Lu
In this paper, we propose binary sparse convolutional networks called BSC-Net for efficient point cloud analysis.
2 code implementations • CVPR 2022 • Xiuwei Xu, Yifan Wang, Yu Zheng, Yongming Rao, Jie zhou, Jiwen Lu
In this paper, we propose a weakly-supervised approach for 3D object detection, which makes it possible to train a strong 3D detector with position-level annotations (i. e. annotations of object centers).