no code implementations • 15 Apr 2024 • Haojun Sun, Chen Tang, Zhi Wang, Yuan Meng, Jingyan Jiang, Xinzhu Ma, Wenwu Zhu
Diffusion models have emerged as preeminent contenders in the realm of generative models.
no code implementations • 3 Jan 2024 • Chen Tang, Yuan Meng, Jiacheng Jiang, Shuzhao Xie, Rongwei Lu, Xinzhu Ma, Zhi Wang, Wenwu Zhu
Conversely, mixed-precision quantization (MPQ) is advocated to compress the model effectively by allocating heterogeneous bit-width for layers.
1 code implementation • 24 Oct 2023 • Yan Lu, Xinzhu Ma, Lei Yang, Tianzhu Zhang, Yating Liu, Qi Chu, Tong He, Yonghui Li, Wanli Ouyang
It models the uncertainty propagation relationship of the geometry projection during training, improving the stability and efficiency of the end-to-end model learning.
no code implementations • 11 Oct 2023 • Chaoqi Liang, Weiqiang Bai, Lifeng Qiao, Yuchen Ren, Jianle Sun, Peng Ye, Hongliang Yan, Xinzhu Ma, WangMeng Zuo, Wanli Ouyang
To address this research gap, we first conducted a series of exploratory experiments and gained several insightful observations: 1) In the fine-tuning phase of downstream tasks, when using K-mer overlapping tokenization instead of K-mer non-overlapping tokenization, both overlapping and non-overlapping pretraining weights show consistent performance improvement. 2) During the pre-training process, using K-mer overlapping tokenization quickly produces clear K-mer embeddings and reduces the loss to a very low level, while using K-mer non-overlapping tokenization results in less distinct embeddings and continuously decreases the loss.
no code implementations • ICCV 2023 • Xinzhu Ma, Yongtao Wang, Yinmin Zhang, Zhiyi Xia, Yuan Meng, Zhihui Wang, Haojie Li, Wanli Ouyang
In this work, we build a modular-designed codebase, formulate strong training recipes, design an error diagnosis toolbox, and discuss current methods for image-based 3D object detection.
1 code implementation • Conference 2022 • Wei Lin, Kunlin Yang, Xinzhu Ma, Junyu Gao, Lingbo Liu, Shinan Liu, Jun Hou, Shuai Yi, Antoni B. Chan
Here we propose a scale-sensitive generalized loss to tackle this problem.
Ranked #5 on Object Counting on FSC147
no code implementations • 15 Aug 2022 • Xinzhu Ma, Yuan Meng, Yinmin Zhang, Lei Bai, Jun Hou, Shuai Yi, Wanli Ouyang
We hope this work can provide insights for the image-based 3D detection community under a semi-supervised setting.
1 code implementation • 13 Jun 2022 • Zengyu Qiu, Xinzhu Ma, Kunlin Yang, Chunya Liu, Jun Hou, Shuai Yi, Wanli Ouyang
Besides, our DPK makes the performance of the student model positively correlated with that of the teacher model, which means that we can further boost the accuracy of students by applying larger teachers.
1 code implementation • 7 Feb 2022 • Xinzhu Ma, Wanli Ouyang, Andrea Simonelli, Elisa Ricci
3D object detection from images, one of the fundamental and challenging problems in autonomous driving, has received increasing attention from both industry and academia in recent years.
1 code implementation • ICLR 2022 • Zhiyu Chong, Xinzhu Ma, Hong Zhang, Yuxin Yue, Haojie Li, Zhihui Wang, Wanli Ouyang
Finally, this LiDAR Net can serve as the teacher to transfer the learned knowledge to the baseline model.
1 code implementation • ICCV 2021 • Yan Lu, Xinzhu Ma, Lei Yang, Tianzhu Zhang, Yating Liu, Qi Chu, Junjie Yan, Wanli Ouyang
In this paper, we propose a Geometry Uncertainty Projection Network (GUP Net) to tackle the error amplification problem at both inference and training stages.
3D Object Detection From Monocular Images Depth Estimation +3
1 code implementation • 29 Jul 2021 • Yinmin Zhang, Xinzhu Ma, Shuai Yi, Jun Hou, Zhihui Wang, Wanli Ouyang, Dan Xu
In this paper, we propose to learn geometry-guided depth estimation with projective modeling to advance monocular 3D object detection.
Ranked #10 on Monocular 3D Object Detection on KITTI Cars Moderate
1 code implementation • CVPR 2021 • Xinzhu Ma, Yinmin Zhang, Dan Xu, Dongzhan Zhou, Shuai Yi, Haojie Li, Wanli Ouyang
Estimating 3D bounding boxes from monocular images is an essential component in autonomous driving, while accurate 3D object detection from this kind of data is very challenging.
3D Object Detection From Monocular Images Autonomous Driving +3
1 code implementation • ECCV 2020 • Xinzhu Ma, Shinan Liu, Zhiyi Xia, Hongwen Zhang, Xingyu Zeng, Wanli Ouyang
Based on this observation, we design an image based CNN detector named Patch-Net, which is more generalized and can be instantiated as pseudo-LiDAR based 3D detectors.
no code implementations • ICCV 2019 • Xinzhu Ma, Zhihui Wang, Haojie Li, Pengbo Zhang, Wanli Ouyang, Xin Fan
To this end, we first leverage a stand-alone module to transform the input data from 2D image plane to 3D point clouds space for a better input representation, then we perform the 3D detection using PointNet backbone net to obtain objects' 3D locations, dimensions and orientations.
no code implementations • 27 Mar 2019 • Xinzhu Ma, Zhihui Wang, Haojie Li, Peng-Bo Zhang, Xin Fan, Wanli Ouyang
To this end, we first leverage a stand-alone module to transform the input data from 2D image plane to 3D point clouds space for a better input representation, then we perform the 3D detection using PointNet backbone net to obtain objects 3D locations, dimensions and orientations.
2 code implementations • 9 Aug 2018 • Yuanzheng Ci, Xinzhu Ma, Zhihui Wang, Haojie Li, Zhongxuan Luo
Scribble colors based line art colorization is a challenging computer vision problem since neither greyscale values nor semantic information is presented in line arts, and the lack of authentic illustration-line art training pairs also increases difficulty of model generalization.